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für maschinelles Lernen: Arten, Funktionsweise und Auswahl"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-4ff6efc751","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EErfahren Sie, wie Modelle für maschinelles Lernen nach Lernansatz, Aufgabe und Architektur klassifiziert werden – und wie Sie bewerten, welche Option am besten zu Ihren Daten und Anforderungen passt.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip_copy":{"id":"content-chip-e5c166951a","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/gulnaz-sharipova/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Biografie lesen"},"image":{"id":"image","isLcpImage":false,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--f7c921fe-d10f-4d90-ac38-c79aff7cf132/gulnaz-photo.jpg?preferwebp=true&quality=85","alt":"Gulnaz Sharipova ","lazyEnabled":true,"height":"512","width":"512",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Gulnaz Sharipova","Lokale Redakteurin, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip","content_chip_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["title_v2","text","container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-dc66f57895",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-c6ad6abdb8","youtubeVideoId":"JDMEUrwpYlE","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"}},":itemsOrder":["youtube"]},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1398138236":{"id":"flexible-column-container-ad5799fc20","propertiesId":"hub-hero-related-topics","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container border-top","layout":"SIMPLE","id":"container-a8de61194e",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-6b2c8e0bf6","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EThemen zu ML-Modellen:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-a851f1e68d","additionalClasses":"related-topics ","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/decision-tree/\"\u003EEntscheidungsbäume\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/linear-regression/\"\u003ELineare Regression\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/random-forest/\"\u003ERandom Forest\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/regression/\"\u003ERegressionsmodell\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/support-vector-machine/\"\u003ESupport Vector Machine\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-8e44217dee","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"callout__0":{"id":"text-70abf7ef9e","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EML-MODELLE DEFINIERT\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EModelle für maschinelles Lernen sind Rechensysteme, die aus Daten gelernte Beziehungen nutzen, um Vorhersagen zu treffen, Muster zu erkennen oder Ausgaben basierend auf neuen Eingaben zu generieren.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-9e37b72351","text":"\u003Cp\u003EWenn Sie heute jemanden bitten, ein Modell für maschinelles Lernen zu nennen, wird er wahrscheinlich ChatGPT oder ein anderes \u003Ca href=\"https://www.snowflake.com/de/fundamentals/large-language-model/\"\u003ELarge Language Model\u003C/a\u003E (LLM) erwähnen. Dennoch verlassen sich die meisten Unternehmen jeden Tag auf viele andere Arten von Modellen – zur Unterstützung von Prognosen, Risikobewertungen, Empfehlungen, Anomalieerkennung und anderen begrenzten Vorhersagen. Ein einzelnes Unternehmen kann Dutzende oder Hunderte von Modellen ausführen, die auf völlig unterschiedlichen mathematischen Grundlagen aufbauen.\u003C/p\u003E\r\n\u003Cp\u003EEines kann eine \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/linear-regression/\"\u003Elineare Regression\u003C/a\u003E mit einer Handvoll Koeffizienten sein, ein anderes ein Gradient-Boosted-Ensemble, das mit Millionen von Datensätzen trainiert wurde, und ein weiteres ein Foundation-Modell mit Milliarden von Parametern. Unterschiedliche Modellfamilien treffen unterschiedliche Annahmen über die Daten, lernen unterschiedliche Arten von Beziehungen und stellen unterschiedliche Anforderungen an \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/model-training/\"\u003ETraining\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/inference/\"\u003EInferenz\u003C/a\u003E und Interpretation.\u003C/p\u003E\r\n\u003Cp\u003EDiese Unterschiede sind nicht nur theoretischer Natur. Sie bestimmen, welche Probleme ein Modell lösen kann, wie viele Daten es benötigt, wie seine Ausgaben bewertet werden können und ob es den Anforderungen der jeweiligen Aufgabe entspricht.\u003C/p\u003E\r\n\u003Cp\u003EDas leistungsstärkste Modell ist nicht immer die beste Wahl. Ein kleineres oder spezialisierteres Modell kann vergleichbare Ergebnisse bei geringeren Kosten, schnellerer Performance oder einfacherer Auswertung liefern. Wie \u003Ca href=\"https://www.snowflake.com/en/blog/engineering/cortex-ai-function-studio-optimization/\"\u003EKyle Schmaus von Snowflake schreibt\u003C/a\u003E: „Die Herausforderung besteht darin, zu bestimmen, wo Frontier-Modelle wirklich notwendig sind und wo optimierte Alternativen das gleiche Ergebnis erzielen können.“ Aus diesem Grund benötigen Unternehmen die Flexibilität, zwischen Modellfamilien wählen zu können. In der Praxis bedeutet dies die Verwaltung eines Portfolios von Modellen, die für unterschiedliche Aufgaben, Datentypen und Performance-Anforderungen ausgewählt wurden.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-a-machine-learning-model":{"id":"title-v2-05e5aec662","additionalClasses":"anchor-title anchor-title--what-is-a-machine-learning-model","type":"heading2","lines":["Was ist ein Modell für maschinelles Lernen?"],":type":"snowflake-site/components/title-v2"},"text_what-is-a-machine-learning-model_0":{"id":"text-38f1447c62","text":"\u003Cp\u003EEin Modell für maschinelles Lernen ist eine trainierte Darstellung von Mustern oder Beziehungen in Daten. Es akzeptiert eine Eingabe, wendet während des Trainings gelernte Parameter an und erzeugt eine Ausgabe wie einen vorhergesagten Wert, eine Klassenwahrscheinlichkeit, eine generierte Sequenz oder eine Gruppenzuordnung.\u003C/p\u003E\n\u003Cp\u003EDas Modell unterscheidet sich von dem Algorithmus, der zu seiner Erstellung verwendet wurde. Der Algorithmus steuert, wie das Lernen abläuft, während das Modell die aus den Daten gelernten Parameter und Strukturen enthält.\u003C/p\u003E\n\u003Cp\u003EEin Entscheidungsbaum-Algorithmus sucht beispielsweise nach Feature-Werten, die Trainingsbeispiele in zunehmend nützliche Gruppen unterteilen. Der angepasste Baum – einschließlich seiner ausgewählten Features, Split-Schwellenwerte und Blattwerte – ist das Modell. Ein weiterer Durchlauf desselben Algorithmus mit anderen Daten kann einen anderen Baum erzeugen.\u003C/p\u003E\n\u003Cp\u003EDiese Unterscheidung trennt auch das Training von der Inferenz. Während des Trainings passt der Lernalgorithmus die Parameter des Modells an, um einen Fehler zu reduzieren oder ein anderes Ziel zu verbessern. Während der Inferenz wendet das trainierte Modell diese Parameter auf neue Daten an. Ein Modell, das das Risiko eines Geräteausfalls schätzt, könnte aus historischen Sensormesswerten und Wartungsaufzeichnungen lernen und dann stündlich eine Inferenz durchführen, wenn neue Messwerte eintreffen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"svg_algorithm-vs-model":{"id":"text-d5ef461b06","text":"\u003Csvg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 968 751\" width=\"100%\" role=\"img\" aria-labelledby=\"dgm-an-algorithm-compared-with-t-ccff-title dgm-an-algorithm-compared-with-t-ccff-desc\" style=\"display:block;max-width:760px;margin:2rem auto;height:auto;\"\u003E\u003Ctitle id=\"dgm-an-algorithm-compared-with-t-ccff-title\"\u003EEin Algorithmus im Vergleich zu dem von ihm erzeugten Modell\u003C/title\u003E\u003Cdesc id=\"dgm-an-algorithm-compared-with-t-ccff-desc\"\u003EDie Unterscheidung, die Leser am häufigsten übersehen, und die erste FAQ der Seite. Vergleich von Algorithmus und Modell. Algorithmus: Das Verfahren, das aus Daten lernt; Sucht nach Splits, die Beispiele trennen; Wird erneut mit neuen Daten ausgeführt, erzeugt ein anderes Ergebnis; Wird vor Beginn des Trainings ausgewählt. Modell: Das trainierte Artefakt, das das Verfahren erzeugt; Speichert Split-Regeln, Koeffizienten oder Gewichte; Ist nach der Anpassung fixiert, bis es neu trainiert wird; Führt Inferenz für neue Eingaben aus.\u003C/desc\u003E\u003Crect x=\"0\" y=\"0\" width=\"469\" height=\"94\" rx=\"12\" fill=\"#11253F\"/\u003E\u003Ctext x=\"32\" y=\"62\" font-family=\"Lato, Arial, sans-serif\" font-size=\"38\" font-weight=\"700\" fill=\"#FFFFFF\"\u003E\u003Ctspan x=\"32\"\u003EAlgorithmus\u003C/tspan\u003E\u003C/text\u003E\u003Crect x=\"499\" y=\"0\" width=\"469\" height=\"94\" rx=\"12\" fill=\"#11567F\"/\u003E\u003Ctext x=\"531\" y=\"62\" font-family=\"Lato, Arial, sans-serif\" font-size=\"38\" font-weight=\"700\" fill=\"#FFFFFF\"\u003E\u003Ctspan x=\"531\"\u003EModell\u003C/tspan\u003E\u003C/text\u003E\u003Crect x=\"0\" y=\"124\" width=\"469\" height=\"125\" rx=\"12\" fill=\"#F6F9FA\"/\u003E\u003Ctext x=\"32\" y=\"180\" font-family=\"Lato, Arial, sans-serif\" font-size=\"30\" font-weight=\"700\" fill=\"#11253F\"\u003E\u003Ctspan x=\"32\"\u003EDas Verfahren, das\u003C/tspan\u003E\u003Ctspan x=\"32\" dy=\"37\"\u003Eaus Daten lernt\u003C/tspan\u003E\u003C/text\u003E\u003Crect x=\"499\" y=\"124\" width=\"469\" height=\"125\" rx=\"12\" fill=\"#F6F9FA\"/\u003E\u003Ctext x=\"531\" y=\"180\" font-family=\"Lato, Arial, sans-serif\" font-size=\"30\" font-weight=\"700\" fill=\"#11253F\"\u003E\u003Ctspan x=\"531\"\u003EDas trainierte Artefakt, das das\u003C/tspan\u003E\u003Ctspan x=\"531\" dy=\"37\"\u003EVerfahren erzeugt\u003C/tspan\u003E\u003C/text\u003E\u003Crect x=\"0\" y=\"279\" width=\"469\" height=\"125\" rx=\"12\" fill=\"#F6F9FA\"/\u003E\u003Ctext x=\"32\" y=\"335\" font-family=\"Lato, Arial, sans-serif\" font-size=\"30\" font-weight=\"700\" fill=\"#11253F\"\u003E\u003Ctspan x=\"32\"\u003ESucht nach Splits, die\u003C/tspan\u003E\u003Ctspan x=\"32\" dy=\"37\"\u003EBeispiele trennen\u003C/tspan\u003E\u003C/text\u003E\u003Crect x=\"499\" y=\"279\" width=\"469\" height=\"125\" rx=\"12\" fill=\"#F6F9FA\"/\u003E\u003Ctext x=\"531\" y=\"335\" font-family=\"Lato, Arial, sans-serif\" font-size=\"30\" font-weight=\"700\" fill=\"#11253F\"\u003E\u003Ctspan x=\"531\"\u003ESpeichert Split-Regeln,\u003C/tspan\u003E\u003Ctspan x=\"531\" dy=\"37\"\u003EKoeffizienten oder Gewichte\u003C/tspan\u003E\u003C/text\u003E\u003Crect x=\"0\" y=\"434\" width=\"469\" height=\"162\" rx=\"12\" fill=\"#F6F9FA\"/\u003E\u003Ctext x=\"32\" y=\"490\" font-family=\"Lato, Arial, sans-serif\" font-size=\"30\" font-weight=\"700\" fill=\"#11253F\"\u003E\u003Ctspan x=\"32\"\u003EWird erneut mit neuen Daten ausgeführt,\u003C/tspan\u003E\u003Ctspan x=\"32\" dy=\"37\"\u003Eerzeugt ein anderes\u003C/tspan\u003E\u003Ctspan x=\"32\" dy=\"37\"\u003EErgebnis\u003C/tspan\u003E\u003C/text\u003E\u003Crect x=\"499\" y=\"434\" width=\"469\" height=\"162\" rx=\"12\" fill=\"#F6F9FA\"/\u003E\u003Ctext x=\"531\" y=\"490\" font-family=\"Lato, Arial, sans-serif\" font-size=\"30\" font-weight=\"700\" fill=\"#11253F\"\u003E\u003Ctspan x=\"531\"\u003EIst nach der Anpassung fixiert, bis\u003C/tspan\u003E\u003Ctspan x=\"531\" dy=\"37\"\u003Ees neu trainiert wird\u003C/tspan\u003E\u003C/text\u003E\u003Crect x=\"0\" y=\"626\" width=\"469\" height=\"125\" rx=\"12\" fill=\"#F6F9FA\"/\u003E\u003Ctext x=\"32\" y=\"682\" font-family=\"Lato, Arial, sans-serif\" font-size=\"30\" font-weight=\"700\" fill=\"#11253F\"\u003E\u003Ctspan x=\"32\"\u003EWird vor Beginn des Trainings\u003C/tspan\u003E\u003Ctspan x=\"32\" dy=\"37\"\u003Eausgewählt\u003C/tspan\u003E\u003C/text\u003E\u003Crect x=\"499\" y=\"626\" width=\"469\" height=\"125\" rx=\"12\" fill=\"#F6F9FA\"/\u003E\u003Ctext x=\"531\" y=\"682\" font-family=\"Lato, Arial, sans-serif\" font-size=\"30\" font-weight=\"700\" fill=\"#11253F\"\u003E\u003Ctspan x=\"531\"\u003EFührt Inferenz für neue\u003C/tspan\u003E\u003Ctspan x=\"531\" dy=\"37\"\u003EEingaben aus\u003C/tspan\u003E\u003C/text\u003E\u003C/svg\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_what-is-a-machine-learning-model_1":{"id":"text-9f0ba98989","text":"\u003Cp\u003EParameter nehmen in verschiedenen Modellfamilien unterschiedliche Formen an. Bei der linearen Regression umfassen sie den Koeffizienten, der jedem Eingabe-Feature zugewiesen ist. Ein Baum speichert Split-Regeln und Vorhersagewerte. Ein \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/neural-network/\"\u003Eneuronales Netzwerk\u003C/a\u003E kann Millionen oder Milliarden von Gewichten enthalten, die über Schichten verteilt sind. Unabhängig von ihrer Struktur machen diese gelernten Werte einen Großteil dessen aus, was das Modell aus dem Training behält.\u003C/p\u003E\n\u003Cp\u003EEin Produktionsmodell umfasst in der Regel mehr als nur gelernte Parameter. Es kann auch Vorverarbeitungslogik, erwartete Eingabeschemata, Klassen-Labels, Abhängigkeitsinformationen und Metadaten enthalten, die erforderlich sind, um Vorhersagen zuverlässig zu reproduzieren.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003ESehen Sie sich an, wie Tuhin Ghosh von Coinbase erklärt, wie Snowflake ML-Funktionen die Art und Weise vereinfachen, wie Coinbase maschinelles Lernen in großem Maßstab bereitstellt:\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-is-a-machine-learning-model_0":{"id":"embed-b068723462","youtubeVideoId":"Eh33vfAP30U","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_how-machine-learning-models-work":{"id":"title-v2-b43ff05941","additionalClasses":"anchor-title anchor-title--how-machine-learning-models-work","type":"heading2","lines":["Wie Modelle für maschinelles Lernen funktionieren"],":type":"snowflake-site/components/title-v2"},"text_how-machine-learning-models-work_0":{"id":"text-2cc9b03232","text":"\u003Cp\u003EEin typischer Workflow für \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/\"\u003Emaschinelles Lernen\u003C/a\u003E beginnt mit einem Dataset, das für die Aufgabe relevante Beobachtungen enthält. Jede Beobachtung wird durch Features dargestellt, die Eingabevariablen, die dem Modell zur Verfügung stehen. Beim überwachten Lernen enthalten die Daten auch ein Label oder einen Zielwert, der die gewünschte Ausgabe darstellt. Ein Kreditrisiko-Dataset könnte beispielsweise Features enthalten, die Einkommen, Zahlungshistorie und bestehende Schulden beschreiben, zusammen mit einem Label, das angibt, ob ein früherer Kreditnehmer einen Kredit zurückgezahlt hat.\u003C/p\u003E\n\u003Cp\u003EBevor das Training beginnt, bereinigen und transformieren Fachleute in der Regel die Daten, konstruieren nützliche Features und unterteilen die verfügbaren Beispiele in Trainings-, Validierungs- und Test-Sets. Diese Partitionen dienen unterschiedlichen Zwecken: Das Trainings-Set passt das Modell an, das Validierungs-Set unterstützt die Modell- und Hyperparameterauswahl, und das Test-Set liefert eine endgültige Schätzung unter Verwendung von Daten, die die Entwicklung nicht gesteuert haben.\u003C/p\u003E\n\u003Cp\u003EDer Trainingsalgorithmus leitet dann Beispiele durch das Modell und vergleicht dessen Ausgabe mit dem gewünschten Ergebnis. Eine Verlustfunktion wandelt die Differenz in einen numerischen Wert um. Die Optimierung passt die Parameter an, um diesen Verlust zu reduzieren, obwohl sich der genaue Anpassungsprozess zwischen den Modellfamilien erheblich unterscheidet. Ein lineares Modell kann Koeffizienten durch eine analytische Lösung oder iterative Optimierung schätzen, während ein neuronales Netzwerk typischerweise Gradienten berechnet und diese rückwärts durch seine Schichten propagiert.\u003C/p\u003E\n\u003Cp\u003EFachleute legen auch Hyperparameter fest, die den Lernprozess oder die Modellstruktur steuern, anstatt direkt aus den Trainingsbeispielen gelernt zu werden. Dazu können die maximale Tiefe eines Baums, die Anzahl der Bäume in einem \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/random-forest/\"\u003ERandom Forest\u003C/a\u003E, eine Regularisierungsstrafe oder die Lernrate gehören, die zur Aktualisierung der Gewichte des neuronalen Netzwerks verwendet wird.\u003C/p\u003E\n\u003Cp\u003ENach der Anpassung wird das Modell anhand zurückgehaltener Daten bewertet. Die relevante Metrik hängt von der Aufgabe und den Folgen ihrer Fehler ab. Die Genauigkeit kann nützlich sein, wenn die Klassen einigermaßen ausgewogen sind und die Kosten für verschiedene Fehler ähnlich sind, während Präzision, Recall, Kalibrierung oder die Fläche unter einer Kurve andere Systeme möglicherweise besser beschreiben. Regressionsaufgaben können den mittleren absoluten Fehler, den quadratischen Fehler oder eine domänenspezifische Kostenfunktion verwenden. Die Auswahl der Metrik ist Teil der Problemdefinition: Zwei Modelle können ihren Rang ändern, wenn sie anhand unterschiedlicher Maße bewertet werden.\u003C/p\u003E\n\u003Cp\u003EDas Ziel ist die Generalisierung – die Performance bei neuen Beispielen, die aus den Bedingungen stammen, auf die das Modell voraussichtlich stoßen wird. Ein Modell kann sich eng an seine Trainingsdaten anpassen, ohne Beziehungen zu lernen, die darüber hinausgehen, was zu schlechten Ergebnissen in der Produktion führt. Diese Lücke ist der Grund, warum die Evaluierung Beispiele verwenden muss, die von der Anpassung ausgeschlossen sind, und warum die Performance in der Produktion auch nach der Bereitstellung noch überwacht werden muss.\u003C/p\u003E\n\u003Cp\u003EDie Modellqualität wird auch durch die in den Daten verfügbaren Informationen begrenzt. Fehlende Variablen, inkonsistente Messungen, ungenaue Labels und nicht repräsentative Stichproben können dem Modell Beziehungen beibringen, die in der Produktion nicht gelten. Verschiedene Algorithmen tolerieren Rauschen, Ausreißer und fehlende Werte unterschiedlich, aber kein Modell kann ein Signal wiederherstellen, das die Daten nie erfasst haben.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_how-machine-learning-models-work_0":{"id":"card-v2-fb89ffeee9","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/video/bama/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Customer Story ansehen"},"text":{"id":"text","text":"\u003Cp\u003EDer norwegische Obst- und Gemüsehändler BAMA nutzt Snowflake, um Daten über seine gesamte Wertschöpfungskette für Frischprodukte hinweg zu zentralisieren und so Daten für Governance, Data Science, maschinelles Lernen, Orchestrierung, Modell-Hosting und Training zugänglicher zu machen. Mit Snowflake und Snowpark ML hat BAMA ein Währungsprognosemodell entwickelt, das dem Finanzteam hilft, den zukünftigen Bedarf an Euro, Dollar und britischen Pfund vorherzusagen, wodurch Cashflow-bezogene Verluste um 35 % reduziert und ein geschätzter Wert von bis zu 700.000 USD [Stand April 2025] erzielt wurden.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal",":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["BAMA"],":type":"snowflake-site/components/title-v2"},"image":{"id":"image","isLcpImage":false,"alt":"BAMA","lazyEnabled":true,":type":"snowflake-site/components/image"},"type":"content-card"},"title_ways-to-classify-machine-learning-models":{"id":"title-v2-b8bea6580e","additionalClasses":"anchor-title anchor-title--ways-to-classify-machine-learning-models","type":"heading2","lines":["Möglichkeiten zur Klassifizierung von Modellen für maschinelles Lernen"],":type":"snowflake-site/components/title-v2"},"text_ways-to-classify-machine-learning-models_0":{"id":"text-bad95036db","text":"\u003Cp\u003EEs gibt keine endgültige Liste von Modelltypen für maschinelles Lernen, da Modelle nach mehreren Dimensionen gruppiert werden können: nach Lernansatz, nach Aufgabe und nach Modellfamilie.\u003C/p\u003E\n\u003Cp\u003EEin Random Forest ist beispielsweise eine baumbasierte Modellfamilie. Er verwendet im Allgemeinen überwachtes Lernen und kann entweder Klassifizierung oder Regression durchführen. Diese Dimensionen getrennt zu halten, macht es einfacher zu verstehen, wie Modelle zueinander in Beziehung stehen.\u003C/p\u003E\n\u003Ch3\u003ENach Lernansatz\u003C/h3\u003E\n\u003Cp\u003ELernansätze unterscheiden sich je nach Art des Feedbacks, das während des Trainings verfügbar ist. Einige Modelle lernen aus gelabelten Beispielen, während andere Strukturen in ungelabelten Daten identifizieren, Ziele aus den Daten selbst ableiten oder sich durch Belohnungen aus einer Umgebung verbessern.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EÜberwachtes Lernen\u003C/b\u003E trainiert ein Modell anhand von Beispielen, die sowohl Eingaben als auch bekannte Zielwerte enthalten. Es unterstützt Aufgaben wie Klassifizierung, Regression und Ranking.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EUnüberwachtes Lernen\u003C/b\u003E funktioniert ohne vordefinierte Ziel-Labels. Stattdessen identifiziert das Modell Strukturen in den Eingaben, etwa durch die Gruppierung ähnlicher Beobachtungen, die Schätzung einer Datenverteilung oder das Erlernen einer niedrigerdimensionalen Repräsentation.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ETeilüberwachtes Lernen\u003C/b\u003E kombiniert eine kleinere Sammlung gelabelter Beispiele mit einer größeren Sammlung ungelabelter Daten. Es ist nützlich, wenn Eingaben im Überfluss vorhanden sind, aber das Labeling durch Expert:innen teuer oder langsam ist.\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/fundamentals/self-supervised-learning/\"\u003E\u003Cb\u003ESelbstüberwachtes Lernen\u003C/b\u003E\u003C/a\u003E leitet Trainingsziele aus den Daten selbst ab. Ein Sprachmodell kann lernen, fehlende oder nachfolgende Token vorherzusagen, während ein Bildmodell durch die Rekonstruktion maskierter Bereiche oder den Vergleich transformierter Ansichten desselben Bildes lernen kann. Pre-Training\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/reinforcement-learning/\"\u003E\u003Cb\u003EBestärkendes Lernen\u003C/b\u003E\u003C/a\u003E trainiert einen Agenten durch Interaktionen mit einer Umgebung. Anstatt aus einem festen Label für jedes Beispiel zu lernen, erhält der Agent Belohnungen, die mit Aktionen verbunden sind, und erlernt eine Richtlinie, die darauf abzielt, die kumulative Belohnung zu maximieren.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EEinführende Erklärungen bezeichnen überwachtes, unüberwachtes, teilüberwachtes und bestärkendes Lernen manchmal als die „vier Arten für maschinelles Lernen”. Das bleibt eine nützliche High-Level-Antwort, vorausgesetzt, sie werden als Lernansätze und nicht als vier erschöpfende Modelltypen identifiziert.\u003C/p\u003E\n\u003Ch3\u003ENach Aufgabe\u003C/h3\u003E\n\u003Cp\u003EModelle können auch nach der Art der Ausgabe gruppiert werden, die sie erzeugen. Dieselbe Familie kann mehrere Aufgaben unterstützen, darunter Klassifizierung, Regression, Clustering, Ranking, Forecasting und Generierung.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EKlassifizierung\u003C/b\u003E ordnet eine Eingabe einer oder mehreren Kategorien zu. Ein Modell könnte eine Transaktion als legitim oder potenziell betrügerisch klassifizieren, ein Objekt in einem Bild identifizieren oder eine Support-Anfrage an die richtige Warteschlange weiterleiten.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/regression/\"\u003ERegressionsmodelle\u003C/a\u003E\u003C/b\u003E sagen einen kontinuierlichen numerischen Wert voraus, wie z. B. Nachfrage, Temperatur oder Lieferzeit.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EClustering\u003C/b\u003E gruppiert Beobachtungen anhand eines Ähnlichkeitsmaßes, ohne sich auf vordefinierte Kategorie-Labels zu stützen.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ERanking\u003C/b\u003E ordnet mögliche Ergebnisse, wie bei der Suche, bei Empfehlungen oder bei der Lead-Priorisierung.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EPrognose (Forecasting)\u003C/b\u003E schätzt zukünftige Werte und berücksichtigt dabei die zeitliche Reihenfolge der Beobachtungen.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EGenerierung\u003C/b\u003E erzeugt neue Inhalte oder strukturierte Ausgaben, einschließlich Text, Bildern, Audio, Code und molekularen Repräsentationen.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EWeitere Aufgaben umfassen Anomalieerkennung, Dimensionalitätsreduktion, Representation Learning und Steuerung. Ein einzelnes Modell kann auch mehrere Aufgaben unterstützen, insbesondere wenn es mit breiten Daten vortrainiert wurde.\u003C/p\u003E\n\u003Ch3\u003ENach Modellfamilie\u003C/h3\u003E\n\u003Cp\u003EEine Modellfamilie beschreibt die Struktur, durch die das Modell Beziehungen darstellt. Diese Kategorien können sich überschneiden. Ein Autoencoder ist beispielsweise sowohl eine neuronale Netzwerkarchitektur als auch ein Modell zur Dimensionalitätsreduktion, während ein Transformer eine neuronale Netzwerkarchitektur ist, die von vielen Foundation-Modellen verwendet wird. Das Familien-Label verrät Anwender:innen etwas über die Mechanik, die Annahmen und das Rechenprofil des Modells, definiert aber nicht vollständig die Aufgabe, die das Modell ausführen soll.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_common-machine-learning-model-families":{"id":"title-v2-d67c094557","additionalClasses":"anchor-title anchor-title--common-machine-learning-model-families","type":"heading2","lines":["Gängige Modellfamilien für maschinelles Lernen"],":type":"snowflake-site/components/title-v2"},"text_common-machine-learning-model-families_0":{"id":"text-0d1e5174f2","text":"\u003Cp\u003EDie folgenden Familien decken viele der Modelle ab, die für angewandtes maschinelles Lernen verwendet werden. Sie sind eher repräsentativ als erschöpfend, und einige umfassen Algorithmen, die mehr als eine Aufgabe unterstützen.\u003C/p\u003E\n\u003Ch3\u003ELineare Modelle\u003C/h3\u003E\n\u003Cp\u003ELineare Modelle berechnen eine Ausgabe aus einer gewichteten Kombination von Eingabe-Features. Bei der linearen Regression ist die Ausgabe ein kontinuierlicher Wert. Die logistische Regression wendet eine verwandte Formulierung an, um die Wahrscheinlichkeit einer Klasse zu schätzen.\u003C/p\u003E\n\u003Cp\u003EAngenommen, ein Modell schätzt die Lieferzeit anhand von Streckenlänge, Bestellvolumen und Lager-Workload. Ein lineares Modell weist jedem Feature einen Koeffizienten zu, der darstellt, wie sich die vorhergesagte Lieferzeit ändert, wenn sich dieses Feature ändert, vorausgesetzt, die anderen bleiben unverändert.\u003C/p\u003E\n\u003Cp\u003EDiese Struktur macht lineare Modelle zu nützlichen Baselines. Sie trainieren schnell, laufen während der Inferenz effizient und legen Beziehungen offen, die Anwender:innen überprüfen können. Regularisierungsmethoden wie Ridge, Lasso und Elastic Net schränken die Koeffizienten ein, was Overfitting reduzieren und in einigen Fällen das Feature-Set vereinfachen kann.\u003C/p\u003E\n\u003Cp\u003EDieselbe Struktur ist jedoch auch eine Einschränkung. Ein einfaches lineares Modell kann eine gekrümmte Beziehung oder Interaktion nicht erfassen, es sei denn, diese Terme werden als Features bereitgestellt. Wenn die zugrunde liegende Beziehung stark nichtlinear ist, kann ein lineares Modell selbst bei korrekter Konfiguration zu Underfitting führen.\u003C/p\u003E\n\u003Ch3\u003EEntscheidungsbäume\u003C/h3\u003E\n\u003Cp\u003EEin \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/decision-tree/\"\u003EEntscheidungsbaum\u003C/a\u003E stellt eine Vorhersage als eine Sequenz von Feature-basierten Splits dar. An jedem internen Knoten wertet das Modell eine Bedingung aus, z. B. ob ein Kontostand einen erlernten Schwellenwert überschreitet. Der Pfad durch diese Bedingungen führt zu einem Blatt, das eine vorhergesagte Klasse, Wahrscheinlichkeit oder einen numerischen Wert enthält.\u003C/p\u003E\n\u003Cp\u003EBäume können nichtlineare Beziehungen und Interaktionen darstellen, ohne dass Anwender:innen diese im Voraus spezifizieren müssen. Sie können auch mit einer Mischung von Feature-Typen arbeiten und erfordern im Allgemeinen weniger Skalierung oder Normalisierung als distanzbasierte und lineare Methoden.\u003C/p\u003E\n\u003Cp\u003EEin kleiner Baum ist relativ einfach zu überprüfen, da eine Vorhersage durch eine endliche Menge von Regeln verfolgt werden kann, aber die Interpretierbarkeit nimmt ab, je tiefer der Baum wird und je mehr Zweige er entwickelt. Einzelne Bäume können auch instabil sein. Eine bescheidene Änderung in den Trainingsdaten kann einen frühen Split verändern und eine wesentlich andere Struktur erzeugen. Die Einschränkung der Tiefe, die Anforderung von mehr Beispielen pro Blatt oder das Pruning des angepassten Baums können Overfitting reduzieren, obwohl diese Kontrollen das Modell auch daran hindern können, nützliche Komplexität darzustellen.\u003C/p\u003E\n\u003Ch3\u003EEnsemble-Baummodelle\u003C/h3\u003E\n\u003Cp\u003EEnsemble-Methoden kombinieren mehrere Modelle, sodass ihre aggregierte Vorhersage genauer oder stabiler ist als das Ergebnis eines einzelnen Komponentenmodells. Zwei der gängigsten baumbasierten Ansätze sind Random Forests und Gradient Boosting.\u003C/p\u003E\n\u003Cp\u003EEin Random Forest passt viele Entscheidungsbäume an, indem er verschiedene Stichproben der Beobachtungen und zufällige Teilmengen von Kandidaten-Features verwendet. Für die Klassifizierung kombiniert der Forest die Vorhersagen der Bäume, entweder durch eine Mehrheitsentscheidung oder durch Mittelung ihrer vorhergesagten Klassenwahrscheinlichkeiten. Für die Regression werden ihre numerischen Vorhersagen gemittelt. Da die Bäume nicht alle dieselben Fehler machen, reduziert die Aggregation typischerweise die Varianz, die mit einem angepassten Baum verbunden ist.\u003C/p\u003E\n\u003Cp\u003EGradient-Boosted Trees werden sequenziell aufgebaut. Jeder neue Baum konzentriert sich darauf, die vom aktuellen Ensemble hinterlassenen Fehler zu verbessern. Implementierungen wie \u003Ca href=\"https://xgboost.readthedocs.io/en/stable/\" target=\"_blank\"\u003EXGBoost\u003C/a\u003E, \u003Ca href=\"https://lightgbm.readthedocs.io/en/stable/\" target=\"_blank\"\u003ELightGBM\u003C/a\u003E und \u003Ca href=\"https://catboost.ai/\" target=\"_blank\"\u003ECatBoost\u003C/a\u003E fügen verschiedene Optimierungen und Kontrollen hinzu, aber alle verwenden Boosting anstelle der unabhängigeren Baumkonstruktion, die in einem Random Forest zu finden ist.\u003C/p\u003E\n\u003Cp\u003EBaum-Ensembles schneiden häufig gut bei strukturierten tabellarischen Daten ab, bei denen Beobachtungen als Zeilen und Features als Spalten erscheinen. Zu ihren Kompromissen gehören größere Modellartefakte, zusätzliche Trainings- oder Inferenzkosten und eine geringere direkte Interpretierbarkeit im Vergleich zu einem kleinen Baum. Maße für die Feature-Wichtigkeit und Post-hoc-Erklärungsmethoden können Aspekte ihres Verhaltens aufdecken, aber sie verwandeln das Ensemble nicht in ein einfaches Regelwerk.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"svg_tree-based-model-families":{"id":"text-469bcc5c07","text":"\u003Csvg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 968 1127\" width=\"100%\" role=\"img\" aria-labelledby=\"dgm-how-tree-based-models-build-3cfb-title dgm-how-tree-based-models-build-3cfb-desc\" style=\"display:block;max-width:760px;margin:2rem auto;height:auto;\"\u003E\u003Ctitle id=\"dgm-how-tree-based-models-build-3cfb-title\"\u003EWie baumbasierte Modelle auf einem einzelnen Entscheidungsbaum aufbauen\u003C/title\u003E\u003Cdesc id=\"dgm-how-tree-based-models-build-3cfb-desc\"\u003EEnsembles geben die Lesbarkeit eines Baumes auf, um Stabilität und Genauigkeit zu gewinnen. Baumdiagramm. Die Wurzel enthält eine Sequenz von Feature-Splits. Auf dem Zweig, auf dem Bäume parallel angepasst werden, lautet die nächste Frage: Jeder Baum sieht eine andere Stichprobe. Beispiel: Stimmen oder Durchschnitte über Bäume hinweg. Vorteil: Bäume wiederholen nicht denselben Fehler. Auf dem Zweig, auf dem Bäume sequenziell angepasst werden, lautet die nächste Frage: Jeder Baum korrigiert vorherige Fehler. Beispiel: XGBoost, LightGBM, CatBoost. Vorteil: Stark bei tabellarischen Daten.\u003C/desc\u003E\u003Crect x=\"0\" y=\"0\" width=\"968\" height=\"136\" rx=\"12\" fill=\"#11253F\"/\u003E\u003Ctext x=\"32\" y=\"52\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" text-anchor=\"start\" fill=\"#29B5E8\"\u003EEINZELNER BAUM\u003C/text\u003E\u003Ctext x=\"32\" y=\"104\" font-family=\"Lato, Arial, sans-serif\" font-size=\"38\" font-weight=\"700\" fill=\"#FFFFFF\"\u003E\u003Ctspan x=\"32\"\u003EEine Sequenz von Feature-Splits\u003C/tspan\u003E\u003C/text\u003E\u003Ctext x=\"124\" y=\"196\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" text-anchor=\"start\" fill=\"#11567F\"\u003EBÄUME WERDEN PARALLEL ANGEPASST\u003C/text\u003E\u003Crect x=\"110\" y=\"210\" width=\"858\" height=\"134\" rx=\"12\" fill=\"#29B5E8\"/\u003E\u003Ctext x=\"142\" y=\"262\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" text-anchor=\"start\" fill=\"#11253F\"\u003EBAGGING\u003C/text\u003E\u003Ctext x=\"142\" y=\"312\" font-family=\"Lato, Arial, sans-serif\" font-size=\"36\" font-weight=\"700\" fill=\"#11253F\"\u003E\u003Ctspan x=\"142\"\u003EJeder Baum sieht eine andere Stichprobe\u003C/tspan\u003E\u003C/text\u003E\u003Cg fill=\"none\" stroke=\"#5B9FD4\" stroke-width=\"4\" stroke-linecap=\"round\"\u003E\u003Cpath d=\"M539 344 V380 H317 V418\"/\u003E\u003C/g\u003E\u003Ctext x=\"307\" y=\"374\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" text-anchor=\"end\" fill=\"#11567F\"\u003EBEISPIEL\u003C/text\u003E\u003Crect x=\"110\" y=\"418\" width=\"414\" height=\"165\" rx=\"12\" fill=\"#C4E4F7\"/\u003E\u003Ctext x=\"142\" y=\"470\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" text-anchor=\"start\" fill=\"#11567F\"\u003ERANDOM FOREST\u003C/text\u003E\u003Ctext x=\"142\" y=\"514\" font-family=\"Lato, Arial, sans-serif\" font-size=\"30\" font-weight=\"700\" fill=\"#11253F\"\u003E\u003Ctspan x=\"142\"\u003EStimmen oder Durchschnitte\u003C/tspan\u003E\u003Ctspan x=\"142\" dy=\"37\"\u003Eüber Bäume hinweg\u003C/tspan\u003E\u003C/text\u003E\u003Cg fill=\"none\" stroke=\"#5B9FD4\" stroke-width=\"4\" stroke-linecap=\"round\"\u003E\u003Cpath d=\"M539 344 V380 H761 V418\"/\u003E\u003C/g\u003E\u003Ctext x=\"771\" y=\"374\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" text-anchor=\"start\" fill=\"#11567F\"\u003EVORTEIL\u003C/text\u003E\u003Crect x=\"554\" y=\"418\" width=\"414\" height=\"165\" rx=\"12\" fill=\"#C4E4F7\"/\u003E\u003Ctext x=\"586\" y=\"470\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" text-anchor=\"start\" fill=\"#11567F\"\u003EGERINGERE VARIANZ\u003C/text\u003E\u003Ctext x=\"586\" y=\"514\" font-family=\"Lato, Arial, sans-serif\" font-size=\"30\" font-weight=\"700\" fill=\"#11253F\"\u003E\u003Ctspan x=\"586\"\u003EBäume wiederholen nicht\u003C/tspan\u003E\u003Ctspan x=\"586\" dy=\"37\"\u003E denselben Fehler\u003C/tspan\u003E\u003C/text\u003E\u003Ctext x=\"124\" y=\"643\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" text-anchor=\"start\" fill=\"#11567F\"\u003EBÄUME WERDEN SEQUENZIELL ANGEPASST\u003C/text\u003E\u003Crect x=\"110\" y=\"657\" width=\"858\" height=\"134\" rx=\"12\" fill=\"#29B5E8\"/\u003E\u003Ctext x=\"142\" y=\"709\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" text-anchor=\"start\" fill=\"#11253F\"\u003EBOOSTING\u003C/text\u003E\u003Ctext x=\"142\" y=\"759\" font-family=\"Lato, Arial, sans-serif\" font-size=\"36\" font-weight=\"700\" fill=\"#11253F\"\u003E\u003Ctspan x=\"142\"\u003EJeder Baum korrigiert vorherige Fehler\u003C/tspan\u003E\u003C/text\u003E\u003Cg fill=\"none\" stroke=\"#5B9FD4\" stroke-width=\"4\" stroke-linecap=\"round\"\u003E\u003Cpath d=\"M539 791 V827 H317 V865\"/\u003E\u003C/g\u003E\u003Ctext x=\"307\" y=\"821\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" text-anchor=\"end\" fill=\"#11567F\"\u003EBEISPIEL\u003C/text\u003E\u003Crect x=\"110\" y=\"865\" width=\"414\" height=\"165\" rx=\"12\" fill=\"#C4E4F7\"/\u003E\u003Ctext x=\"142\" y=\"917\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" text-anchor=\"start\" fill=\"#11567F\"\u003EGRADIENT BOOSTING\u003C/text\u003E\u003Ctext x=\"142\" y=\"961\" font-family=\"Lato, Arial, sans-serif\" font-size=\"30\" font-weight=\"700\" fill=\"#11253F\"\u003E\u003Ctspan x=\"142\"\u003EXGBoost, LightGBM,\u003C/tspan\u003E\u003Ctspan x=\"142\" dy=\"37\"\u003ECatBoost\u003C/tspan\u003E\u003C/text\u003E\u003Cg fill=\"none\" stroke=\"#5B9FD4\" stroke-width=\"4\" stroke-linecap=\"round\"\u003E\u003Cpath d=\"M539 791 V827 H761 V865\"/\u003E\u003C/g\u003E\u003Ctext x=\"771\" y=\"821\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" text-anchor=\"start\" fill=\"#11567F\"\u003EVORTEIL\u003C/text\u003E\u003Crect x=\"554\" y=\"865\" width=\"414\" height=\"165\" rx=\"12\" fill=\"#C4E4F7\"/\u003E\u003Ctext x=\"586\" y=\"917\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" text-anchor=\"start\" fill=\"#11567F\"\u003EHÖHERE GENAUIGKEIT\u003C/text\u003E\u003Ctext x=\"586\" y=\"961\" font-family=\"Lato, Arial, sans-serif\" font-size=\"30\" font-weight=\"700\" fill=\"#11253F\"\u003E\u003Ctspan x=\"586\"\u003EStark bei tabellarischen\u003C/tspan\u003E\u003Ctspan x=\"586\" dy=\"37\"\u003EDaten\u003C/tspan\u003E\u003C/text\u003E\u003Cg fill=\"none\" stroke=\"#5B9FD4\" stroke-width=\"4\" stroke-linecap=\"round\"\u003E\u003Cpath d=\"M40 136 V724\"/\u003E\u003Cpath d=\"M40 277 H110\"/\u003E\u003Cpath d=\"M40 724 H110\"/\u003E\u003C/g\u003E\u003Cg fill=\"none\" stroke=\"#5B9FD4\" stroke-width=\"4\" stroke-linecap=\"round\"\u003E\u003Cpath d=\"M0 1104 H56\"/\u003E\u003C/g\u003E\u003Ctext x=\"70\" y=\"1114\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" text-anchor=\"start\" fill=\"#11567F\"\u003EZWEIG\u003C/text\u003E\u003Ctext x=\"196\" y=\"1114\" font-family=\"Lato, Arial, sans-serif\" font-size=\"27\" font-weight=\"400\" text-anchor=\"start\" fill=\"#6B7680\" font-style=\"italic\"\u003Ewie das Ensemble seine Bäume anpasst\u003C/text\u003E\u003C/svg\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_common-machine-learning-model-families_1":{"id":"text-a131830883","text":"\u003Ch3\u003ESupport Vector Machines\u003C/h3\u003E\n\u003Cp\u003EEine \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/support-vector-machine/\"\u003ESupport Vector Machine\u003C/a\u003E (SVM) erlernt eine Grenze, die Klassen trennt, und maximiert dabei den Spielraum zwischen dieser Grenze und den nächstgelegenen Trainingsbeispielen. Diese nahegelegenen Beispiele, bekannt als Support-Vektoren, bestimmen die angepasste Entscheidungsgrenze.\u003C/p\u003E\n\u003Cp\u003EWenn die Klassen nicht durch eine gerade Linie oder eine \u003Ca href=\"https://r4ds.github.io/bookclub-islr/hyperplane.html\" target=\"_blank\"\u003Eflache Hyperebene\u003C/a\u003E getrennt werden können, kann eine Kernel-Funktion nichtlineare Beziehungen durch Ähnlichkeiten zwischen Beobachtungen darstellen. Die Support Vector Regression wendet verwandte Mechanismen auf die kontinuierliche Vorhersage an.\u003C/p\u003E\n\u003Cp\u003ESVMs können bei mittelgroßen, hochdimensionalen Daten gut abschneiden, insbesondere wenn die Features bereits eine nützliche Repräsentation bieten. Die Textklassifizierung basierend auf spärlichen Wort- oder Token-Features ist ein traditionelles Beispiel.\u003C/p\u003E\n\u003Cp\u003EDie Methode wird mit zunehmender Anzahl von Trainingsbeispielen unpraktischer, insbesondere bei nichtlinearen Kernels. Die Ergebnisse hängen auch von der Feature-Skalierung und von Entscheidungen wie dem Kernel, der Regularisierungsstärke und den Kernel-Parametern ab. Da die erlernte Grenze durch Support-Vektoren und nicht durch eine kurze Koeffizientenliste oder einen Entscheidungspfad definiert ist, kann die Erklärung einer einzelnen Vorhersage zusätzliche Analysen erfordern.\u003C/p\u003E\n\u003Ch3\u003ENearest-Neighbor-Modelle\u003C/h3\u003E\n\u003Cp\u003ENearest-Neighbor-Methoden sagen anhand der Trainingsbeispiele voraus, die einer neuen Eingabe am ähnlichsten sind. Ein \u003Ca href=\"https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\" target=\"_blank\"\u003Ek-Nearest-Neighbors-Klassifikator\u003C/a\u003E identifiziert eine festgelegte Anzahl der ähnlichsten Trainingsbeispiele und weist basierend auf deren Stimmen eine Klasse zu. Für die Regression kann er deren Zielwerte mitteln.\u003C/p\u003E\n\u003Cp\u003EDies wird manchmal als Lazy-Learning-Ansatz beschrieben, da die Anpassung relativ wenig Parameterschätzung beinhaltet. Stattdessen behält das Modell die Referenzbeispiele bei und führt einen Großteil seiner Arbeit während der Inferenz aus.\u003C/p\u003E\n\u003Cp\u003EDie Methode ist leicht zu verstehen und kann unregelmäßige Entscheidungsgrenzen darstellen, aber ihr Verhalten hängt von der Definition der Distanz ab. Features, die auf sehr unterschiedlichen Skalen gemessen werden, können die Ähnlichkeit verzerren, wenn sie nicht standardisiert sind, und irrelevante Dimensionen können wirklich ähnliche Beispiele weit voneinander entfernt erscheinen lassen.\u003C/p\u003E\n\u003Cp\u003EDie Inferenz kann auch teuer werden, wenn die Referenzdaten wachsen. Indexierungs- und Approximate-Neighbor-Techniken reduzieren die Suchkosten, aber Nearest-Neighbor-Modelle stellen dennoch andere Anforderungen an Speicher und Latenz eines Systems als ein kompaktes lineares Modell oder ein Entscheidungsbaum.\u003C/p\u003E\n\u003Ch3\u003EProbabilistische Modelle\u003C/h3\u003E\n\u003Cp\u003EProbabilistische Modelle stellen Beziehungen durch Wahrscheinlichkeitsverteilungen dar. Anstatt nur eine Klasse oder eine numerische Schätzung zu erstellen, können sie die Wahrscheinlichkeit mehrerer Ergebnisse berechnen oder Unsicherheiten in verborgenen Variablen darstellen.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://scikit-learn.org/stable/modules/naive_bayes.html\" target=\"_blank\"\u003ENaive-Bayes\u003C/a\u003E-Klassifikatoren schätzen eine Klasse anhand der bedingten Wahrscheinlichkeit beobachteter Features und verwenden dabei eine Unabhängigkeitsannahme, die das Fitting effizient macht. \u003Ca href=\"https://scikit-learn.org/stable/modules/mixture.html\" target=\"_blank\"\u003EGaußsche Mischmodelle\u003C/a\u003E beschreiben Daten als Kombination von Wahrscheinlichkeitsverteilungen und werden häufig für das Clustering oder die Dichteschätzung verwendet. \u003Ca href=\"https://www.bayesserver.com/docs/introduction/bayesian-networks/\" target=\"_blank\"\u003EBayessche Netze\u003C/a\u003E stellen bedingte Beziehungen zwischen Variablen dar, während verborgene \u003Ca href=\"https://www.sciencedirect.com/topics/materials-science/markov-model\" target=\"_blank\"\u003EMarkov-Modelle\u003C/a\u003E Sequenzen repräsentieren, deren zugrunde liegende Zustände nicht direkt beobachtet werden.\u003C/p\u003E\n\u003Cp\u003EDiese Modelle sind nützlich, wenn Unsicherheit Teil des Problems und nicht nur ein zufälliges Ergebnis ist. Eine probabilistische Prognose kann beispielsweise eine Verteilung der möglichen Nachfrage anstelle eines einzelnen Punktwerts schätzen.\u003C/p\u003E\n\u003Cp\u003EIhre Annahmen können sie recheneffizient und interpretierbar machen, aber die Performance hängt davon ab, ob diese Annahmen die Daten angemessen beschreiben. Eine vereinfachte Abhängigkeitsstruktur erfasst möglicherweise wichtige Beziehungen nicht, während ein ausdrucksstärkeres probabilistisches Modell schwer zu schätzen sein kann.\u003C/p\u003E\n\u003Ch3\u003ECluster-Modelle\u003C/h3\u003E\n\u003Cp\u003EClustering-Modelle finden Gruppen ähnlicher Beobachtungen ohne Training mit bekannten Klassen-Labels. Da keine einzelne Definition eines Clusters für jedes Dataset passt, suchen verschiedene Methoden nach unterschiedlichen Arten von Strukturen.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html\" target=\"_blank\"\u003EK-Means\u003C/a\u003E weist Beobachtungen einer bestimmten Anzahl von Clustern zu, die jeweils durch einen erlernten \u003Ca href=\"https://engineeringstatics.org/Chapter_07-centroids.html\" target=\"_blank\"\u003EZentroiden\u003C/a\u003E repräsentiert werden. Es funktioniert am besten, wenn die Entfernung zu einem Zentroiden die Zugehörigkeit sinnvoll darstellt und die Cluster weitgehend kompatible Formen und Maßstäbe aufweisen.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://scikit-learn.org/stable/modules/clustering.html\" target=\"_blank\"\u003EHierarchisches Clustering\u003C/a\u003E kombiniert oder teilt Gruppen wiederholt, um eine verschachtelte Struktur zu erstellen. Anwender:innen können diese Hierarchie auf verschiedenen Ebenen untersuchen, anstatt sich von Anfang an auf eine flache Gruppierung festzulegen.\u003C/p\u003E\n\u003Cp\u003EDichtebasierte Methoden, einschließlich \u003Ca href=\"https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html\" target=\"_blank\"\u003EDBSCAN\u003C/a\u003E, identifizieren Regionen mit dicht gepackten Beobachtungen und können isolierte Punkte als Rauschen markieren. Dadurch können sie Cluster-Formen finden, die zentroidbasierte Methoden möglicherweise übersehen, obwohl das Ergebnis von Dichteschwellenwerten abhängt.\u003C/p\u003E\n\u003Cp\u003ECluster haben keine inhärente geschäftliche Bedeutung. Nach dem Fitting müssen Anwender:innen die mit jeder Gruppe verbundenen Features und Beispiele untersuchen, testen, ob das Muster stabil ist, und entscheiden, ob es die beabsichtigte Analyse oder den Workflow unterstützt.\u003C/p\u003E\n\u003Ch3\u003EModelle zur Dimensionalitätsreduktion\u003C/h3\u003E\n\u003Cp\u003EDie Dimensionalitätsreduktion stellt Daten mit weniger Variablen dar und bewahrt gleichzeitig ausgewählte Aspekte ihrer Struktur. Die resultierende Darstellung kann Visualisierung, Rauschunterdrückung, Komprimierung oder nachgelagertes Modelltraining unterstützen.\u003C/p\u003E\n\u003Cp\u003EDie \u003Ca href=\"https://cs357.cs.illinois.edu/textbook/notes/pca.html\" target=\"_blank\"\u003EHauptkomponentenanalyse (PCA)\u003C/a\u003E erlernt neue Achsen, die zunehmend geringere Mengen an Variation in der Eingabe erfassen. Da die Komponenten gewichtete Kombinationen der ursprünglichen Features sind, kann die PCA korrelierte Messungen in einem kleineren Feature-Raum verdichten.\u003C/p\u003E\n\u003Cp\u003EAndere Methoden bewahren andere Eigenschaften. Einige betonen lokale Nachbarschaften für die Visualisierung, während Autoencoder ein neuronales Netzwerk verwenden, um eine Eingabe in eine niedrigerdimensionale Darstellung zu komprimieren und sie zu rekonstruieren.\u003C/p\u003E\n\u003Cp\u003EDie Reduzierung der Anzahl der Features kann Speicher- und Rechenkosten senken oder Rauschen entfernen, aber sie kann auch Informationen verwerfen. Die erlernten Komponenten sind möglicherweise schwerer zu erklären als die ursprünglichen Variablen, und eine Darstellung, die die Gesamtvarianz bewahrt, bewahrt möglicherweise nicht die für die Vorhersageaufgabe wichtigsten Unterscheidungen.\u003C/p\u003E\n\u003Ch3\u003ENeuronale Netze\u003C/h3\u003E\n\u003Cp\u003EEin neuronales Netzwerk besteht aus Schichten, die eine Eingabe durch erlernte Gewichte und nichtlineare Funktionen transformieren. Während des Trainings erzeugt das Netzwerk eine Ausgabe, berechnet einen Verlust und propagiert Gradienten rückwärts, damit ein Optimierer seine Parameter aktualisieren kann.\u003C/p\u003E\n\u003Cp\u003EUnterschiedliche Architekturen erzwingen Strukturen, die für unterschiedliche Daten geeignet sind:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EFeedforward-Netzwerke\u003C/strong\u003E leiten Informationen durch eine Abfolge von Schichten und unterstützen allgemeine Klassifizierungs- und Regressionsaufgaben.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EFaltungsnetzwerke (Convolutional Neural Networks)\u003C/strong\u003E wenden erlernte Filter auf räumliche Eingaben an und werden häufig für Bilder verwendet.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ERekurrente neuronale Netzwerke\u003C/strong\u003E behalten den Zustand über Sequenzen hinweg bei, obwohl Transformer nun viele Aufgaben übernehmen, die früher rekurrenten Architekturen zugewiesen wurden.\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/neural-network/transformer/\"\u003E\u003Cstrong\u003ETransformer\u003C/strong\u003E\u003C/a\u003E verwenden Aufmerksamkeitsmechanismen, um Beziehungen zwischen Elementen in einer Sequenz oder einer anderen tokenisierten Darstellung zu modellieren.\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/neural-network/autoencoder/\"\u003E\u003Cstrong\u003EAutoencoder\u003C/strong\u003E\u003C/a\u003E erlernen komprimierte Darstellungen, indem sie ihre Eingaben rekonstruieren.\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/neural-network/graph-neural-network/\"\u003E\u003Cstrong\u003EGraph Neural Networks\u003C/strong\u003E\u003C/a\u003E tauschen Informationen zwischen verbundenen Knoten aus, um relationale Strukturen zu modellieren.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ENeuronale Netzwerke können komplexe Funktionen erlernen und mit genügend Daten nützliche Darstellungen aus Bildern, Audio, Text und anderen hochdimensionalen Eingaben ableiten. Diese Flexibilität geht oft mit höheren Anforderungen an Daten und Rechenressourcen, einer umfangreicheren Anpassung und einer weniger direkten Interpretierbarkeit als bei einfacheren Modellen einher.\u003C/p\u003E\n\u003Cp\u003EDie Architektur allein bestimmt nicht die Qualität. Datenkonstruktion, Zielsetzung, Optimierung, Regularisierung und Evaluierung beeinflussen alle, ob ein neuronales Netzwerk über seine Trainingsbeispiele hinaus generalisiert.\u003C/p\u003E\n\u003Ch3\u003EZeitreihenmodelle\u003C/h3\u003E\n\u003Cp\u003EZeitreihenmodelle berücksichtigen die Reihenfolge, in der Beobachtungen auftreten. Ein Prognosesystem muss möglicherweise Trends, wiederkehrende saisonale Muster, verzögerte Effekte und externe Variablen darstellen, die sich im Laufe der Zeit ändern.\u003C/p\u003E\n\u003Cp\u003EStatistische Modelle wie \u003Ca href=\"https://phdinds-aim.github.io/time_series_handbook/01_AutoRegressiveIntegratedMovingAverage/01_AutoRegressiveIntegratedMovingAverage.html\" target=\"_blank\"\u003EAutoregressive Integrated Moving Average (ARIMA)\u003C/a\u003E verwenden vergangene Werte und Restmuster, um zukünftige Beobachtungen zu schätzen. Exponentielle Glättungsmethoden modellieren Komponenten wie Niveau, Trend und Saisonalität. Baum-Ensembles können konstruierte Lag- und Rolling-Window-Features verwenden, während rekurrente Netzwerke und Transformer zeitliche Darstellungen aus Sequenzen erlernen können.\u003C/p\u003E\n\u003Cp\u003EDie Auswahl von Zeitreihenmodellen läuft oft darauf hinaus, Genauigkeit, Interpretierbarkeit und Rechenkosten in Einklang zu bringen. \u003Ca href=\"https://www.snowflake.com/en/blog/engineering/time-series-forecasting-comparing-transform-techniques-tree-based-models\"\u003ESchmaus erklärt\u003C/a\u003E: „Klassische Methoden wie ARIMA und exponentielle Glättung sind schnell zu trainieren, hocheffizient und leicht zu interpretieren, während neuronale Methoden eine höhere Genauigkeit aufweisen und exogene Eingaben und Nichtlinearitäten integrieren können, allerdings auf Kosten der Trainings- und Inferenz-Effizienz.“\u003C/p\u003E\n\u003Cp\u003EDie Evaluierung muss auch die zeitliche Reihenfolge wahren. Ein \u003Ca href=\"https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html\" target=\"_blank\"\u003Ezufälliger Train-Test-Split\u003C/a\u003E kann zukünftige Informationen in das Training einfließen lassen und ein unrealistisch günstiges Ergebnis liefern. Anwender:innen verwenden stattdessen zeitbasierte Holdouts oder rollierende Evaluierungsfenster, die der Art und Weise, wie Prognosen erstellt werden, näher kommen.\u003C/p\u003E\n\u003Ch3\u003EFoundation-Modelle\u003C/h3\u003E\n\u003Cp\u003EEin Foundation-Modell wird mit breiten Daten trainiert, typischerweise in erheblichem Umfang, und ist darauf ausgelegt, die Anpassung an viele nachgelagerte Aufgaben zu unterstützen. Die Kategorie umfasst LLMs sowie Modelle, die für Bilder, Audio, biologische Sequenzen und andere Modalitäten trainiert wurden.\u003C/p\u003E\n\u003Cp\u003EDie meisten Foundation-Modelle verwenden neuronale Netzwerke und selbstüberwachtes Pre-Training. Anstatt für jedes Beispiel ein separates, von Menschen erstelltes Label zu sammeln, leitet der Trainingsprozess Ziele aus den Daten ab, wie z. B. die Vorhersage eines fehlenden Tokens oder die Ausrichtung verwandter Bild- und Textdarstellungen.\u003C/p\u003E\n\u003Cp\u003ENach dem Vortraining kann das Modell durch Prompt, Abruf, \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/model-training/fine-tuning/\"\u003EOptimierung (Fine-Tuning)\u003C/a\u003E oder zusätzliche aufgabenspezifische Ebenen angepasst werden. Diese Wiederverwendung unterscheidet es von einem Modell, das ausschließlich für eine begrenzte Vorhersageaufgabe trainiert wurde.\u003C/p\u003E\n\u003Cp\u003EBeispiele sind LLMs wie GPT, Claude, Llama und Mistral sowie multimodale Modelle wie Gemini und Qwen2.5-VL. Die Kategorie umfasst auch Bildgenerierungsmodelle wie Stable Diffusion und FLUX, Sprachmodelle wie Whisper und biologische Foundation-Modelle wie ESM. Obwohl diese Modelle sehr unterschiedliche Aufgaben unterstützen, teilen sie die Eigenschaft eines breiten Pre-Trainings, gefolgt von einer Anpassung an nachgelagerte Anwendungen durch Prompting, Retrieval, Feinanpassung oder zusätzliches Training.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_common-machine-learning-model-families_0":{"id":"text-5b2927ec8f","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EHÄUFIGER FALLSTRICK\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EViele Teams gehen davon aus, dass ein größeres Foundation-Modell automatisch das beste Ergebnis liefert, aber in der Realität kann ein kleineres oder aufgabenspezifisches Modell eine vergleichbare Qualität bei geringerer Latenz, geringeren Kosten und geringerer Evaluierungskomplexität bieten.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-to-choose-the-right-machine-learning-model":{"id":"title-v2-fb79e0fc33","additionalClasses":"anchor-title anchor-title--how-to-choose-the-right-machine-learning-model","type":"heading2","lines":["So wählen Sie das richtige Modell für maschinelles Lernen aus"],":type":"snowflake-site/components/title-v2"},"text_how-to-choose-the-right-machine-learning-model_0":{"id":"text-dc5261e4ea","text":"\u003Cp\u003EDie Modellauswahl ist ein empirischer Prozess. Verschiedene Familien treffen unterschiedliche Annahmen über die Daten und bringen unterschiedliche Kompromisse mit sich, sodass Anwender:innen in der Regel mehrere Kandidaten vergleichen, bevor sie denjenigen auswählen, der am besten zur Aufgabe und zu den betrieblichen Anforderungen passt.\u003C/p\u003E\n\u003Ch3\u003EDefinieren Sie die Aufgabe und die Evaluierungskriterien\u003C/h3\u003E\n\u003Cp\u003EBestimmen Sie zunächst, ob die Ausgabe eine Klasse, ein numerischer Wert, ein Ranking, ein Cluster, eine Prognose oder ein generiertes Ergebnis ist. Identifizieren Sie dann, wie die Ausgabe verwendet wird.\u003C/p\u003E\n\u003Cp\u003EIn einem medizinischen Screening-System beispielsweise ist das Übersehen eines positiven Falls mit ganz anderen Kosten verbunden als das Einsenden eines zusätzlichen Falls zur Überprüfung. Bei der Nachfrageprognose kann eine Unterschätzung teurer sein als eine gleich große Überschätzung. Die Evaluierungsmetrik sollte diese Konsequenzen darstellen, anstatt nur konventionell für die Aufgabe zu sein.\u003C/p\u003E\n\u003Ch3\u003EUntersuchen Sie die verfügbaren Daten\u003C/h3\u003E\n\u003Cp\u003EStichprobengröße, Feature-Typen, Sparsity, Label-Qualität und zeitliche Struktur schränken die praktischen Kandidaten ein.\u003C/p\u003E\n\u003Cp\u003EBaum-Ensembles bieten oft starke Baselines für strukturierte Daten, die gemischte Feature-Beziehungen enthalten. Lineare Modelle funktionieren gut, wenn die Beziehung annähernd linear ist oder Transparenz wichtig ist. SVMs eignen sich möglicherweise für mittelgroße, hochdimensionale Daten, während neuronale Netzwerke oft in Betracht gezogen werden, wenn große Mengen unstrukturierter oder hochdimensionaler Daten verfügbar sind.\u003C/p\u003E\n\u003Cp\u003EDies sind jedoch eher Ausgangspunkte als Auswahlregeln. \u003Ca href=\"https://arxiv.org/abs/2305.02997\" target=\"_blank\"\u003EIn einem Benchmark, der 176 tabellarische Datasets umfasste\u003C/a\u003E, variierte die relative Performance von neuronalen Netzwerken und Gradient-Boosted Trees mit Eigenschaften wie Feature-Verteilungen, und eine bescheidene Anpassung war manchmal wichtiger als die Wahl zwischen den beiden Familien.\u003C/p\u003E\n\u003Ch3\u003ELegen Sie betriebliche Einschränkungen frühzeitig fest\u003C/h3\u003E\n\u003Cp\u003EEin Modell, das die beste Offline-Metrik liefert, kann für seine Produktionsumgebung dennoch ungeeignet sein. Teams müssen möglicherweise Folgendes begrenzen:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003ETrainingszeit und Infrastrukturkosten\u003C/li\u003E\n\u003Cli\u003EInferenzlatenz\u003C/li\u003E\n\u003Cli\u003ESpeicher- und Modellgröße\u003C/li\u003E\n\u003Cli\u003EHäufigkeit und Kosten des Retrainings\u003C/li\u003E\n\u003Cli\u003EHardware-Abhängigkeiten\u003C/li\u003E\n\u003Cli\u003EErklärungsanforderungen\u003C/li\u003E\n\u003Cli\u003EVolumen und Muster von Vorhersageanfragen\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EBetriebliche Kompromisse ändern oft, welches Modell die bessere Wahl ist. Ein Nearest-Neighbor-Modell erfordert möglicherweise wenig Training, verlagert aber mehr Kosten auf die Inferenz, während ein Ensemble die prädiktive Performance auf Kosten von Latenz und Speicher verbessern kann. Ein einfacheres lineares Modell kann daher einfacher zu prüfen, bereitzustellen und neu zu trainieren sein, selbst wenn ein komplexerer Kandidat ein geringfügig besseres Ergebnis liefert.\u003C/p\u003E\n\u003Ch3\u003EEtablieren Sie eine einfache Baseline\u003C/h3\u003E\n\u003Cp\u003EEine Baseline zeigt, ob zusätzliche Komplexität einen sinnvollen Gewinn bringt. Für die Regression kann dies mit der Vorhersage des historischen Durchschnitts und dem anschließenden Fitting eines linearen Modells beginnen. Für die Klassifizierung könnten Anwender:innen eine Mehrheitsklassenregel, logistische Regression und einen flachen Entscheidungsbaum vergleichen, bevor sie Ensembles oder neuronale Netze hinzufügen.\u003C/p\u003E\n\u003Cp\u003EDie Baseline testet auch die Pipeline. Unerwartet starke Ergebnisse können auf Leakage hinweisen, während unerwartet schwache Ergebnisse Probleme bei Labels, Joins, der Feature-Konstruktion oder der Evaluierung aufdecken können.\u003C/p\u003E\n\u003Ch3\u003EModelle unter konsistenten Bedingungen vergleichen\u003C/h3\u003E\n\u003Cp\u003EKandidatenmodelle sollten dieselben Datenpartitionen und Evaluierungsverfahren verwenden. Die \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/model-training/hyperparameter-tuning/\"\u003EAnpassung der Hyperparameter\u003C/a\u003E muss ohne Rücksprache mit dem finalen Testset erfolgen, da wiederholte Entscheidungen aufgrund der Testperformance dieses Set in eine weitere Quelle für Trainingsfeedback verwandeln.\u003C/p\u003E\n\u003Cp\u003EKreuzvalidierung kann abschätzen, wie die Performance über verschiedene Teilmengen der Daten hinweg variiert, insbesondere wenn die verfügbare Stichprobe begrenzt ist. Zeitabhängige Daten erfordern Aufteilungen, die die Chronologie bewahren.\u003C/p\u003E\n\u003Cp\u003EDer Modellvergleich sollte auch die Variabilität einbeziehen. Ein leicht höherer Durchschnittswert ist möglicherweise nicht aussagekräftig, wenn die Ergebnisse über Folds, Seeds oder Zeitfenster hinweg stark schwanken.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-to-choose-the-right-machine-learning-model_0":{"id":"text-d2cb17fcae","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EKURZER TIPP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EVerwenden Sie für jeden Kandidaten dieselben Datenaufteilungen, Vorverarbeitungsschritte und Evaluierungsmetriken. Andernfalls könnten Unterschiede im Experiment fälschlicherweise für Unterschiede in der Modellqualität gehalten werden.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_how-to-choose-the-right-machine-learning-model_1":{"id":"text-7727afa16a","text":"\u003Ch3\u003EUnderfitting und Overfitting ausbalancieren\u003C/h3\u003E\n\u003Cp\u003EEin Underfit-Modell ist zu stark eingeschränkt, um wichtige Beziehungen zu erfassen. Ein Overfit-Modell lernt Muster, die spezifisch für seine Trainingsstichprobe sind, und funktioniert bei neuen Daten weniger zuverlässig.\u003C/p\u003E\n\u003Cp\u003EDies wird oft durch den Bias-Varianz-Kompromiss beschrieben. Schätzer mit hohem Bias neigen zu systematischen Vereinfachungen, während Schätzer mit hoher Varianz stark auf Änderungen im Trainingsset reagieren. Modellfamilie, Hyperparameter, Regularisierung und Trainingsdatenvolumen beeinflussen alle dieses Gleichgewicht.\u003C/p\u003E\n\u003Cp\u003EDas Ziel ist nicht, das flexibelste Modell auszuwählen, sondern vielmehr genug Flexibilität zu finden, um das nützliche Signal darzustellen, ohne Rauschen anzupassen oder betriebliche Kosten zu verursachen, die den Gewinn nicht rechtfertigen.\u003C/p\u003E\n\u003Ch3\u003EUnter erwarteten Produktionsbedingungen testen\u003C/h3\u003E\n\u003Cp\u003EVor der Bereitstellung sollte die Evaluierung Bedingungen umfassen, auf die das Modell wahrscheinlich stoßen wird: fehlende Felder, neue Kategorien, verzögerte Features, ungewöhnliche Volumina und Verschiebungen in der Eingabeverteilung.\u003C/p\u003E\n\u003Cp\u003EAuch der Serving-Pfad verdient es, getestet zu werden. Die während des Trainings angewendete Vorverarbeitung muss während der Inferenz reproduziert werden, Feature-Definitionen müssen konsistent bleiben und die Latenz sollte über den gesamten Anfragepfad gemessen werden, anstatt nur die Modellberechnung allein.\u003C/p\u003E\n\u003Cp\u003EDie Auswahl bleibt nach dem Start vorläufig. Sobald das Modell Produktionsdaten ausgesetzt ist, kann das Monitoring zeigen, ob Eingaben, Vorhersagen und Ergebnisse noch den Bedingungen entsprechen, unter denen es evaluiert wurde.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_limitations-of-machine-learning-models":{"id":"title-v2-f9f1e65fcc","additionalClasses":"anchor-title anchor-title--limitations-of-machine-learning-models","type":"heading2","lines":["Einschränkungen von Modellen für maschinelles Lernen"],":type":"snowflake-site/components/title-v2"},"text_limitations-of-machine-learning-models_0":{"id":"text-0203b9820b","text":"\u003Cp\u003EEin Modell für maschinelles Lernen kodiert Muster in seinen Trainingsdaten. Es bestimmt nicht unabhängig, ob diese Muster vollständig, kausal oder für die getroffene Entscheidung angemessen sind.\u003C/p\u003E\n\u003Ch3\u003EUnvollständige Repräsentation\u003C/h3\u003E\n\u003Cp\u003ETrainingsdaten sind eine Stichprobe vergangener Beobachtungen, die von bestehenden Systemen und Prozessen erzeugt wurden. Sie können relevante Populationen, seltene Bedingungen oder Variablen auslassen, die nicht aufgezeichnet wurden. Ein Modell, das mit dieser Stichprobe trainiert wurde, kann auf einer zurückgehaltenen Teilmenge gut abschneiden, während es in Umgebungen, die die Quelldaten kaum repräsentierten, unzuverlässig bleibt.\u003C/p\u003E\n\u003Ch3\u003EOverfitting und Verteilungsverschiebung\u003C/h3\u003E\n\u003Cp\u003ESelbst ein sorgfältig evaluiertes Modell kann auf Produktionsdaten stoßen, die sich von seinen Trainingsdaten unterscheiden. Das Kundenverhalten ändert sich, Sensoren werden ausgetauscht, vorgelagerte Definitionen werden überarbeitet und neue Produkte verändern die Feature-Verteilung. Einige Verschiebungen verringern die Gesamt-Performance, während andere nur eine bestimmte Population oder Betriebsbedingung betreffen. Aus diesem Grund kann ein aggregiertes Monitoring eine lokalisierte Verschlechterung übersehen.\u003C/p\u003E\n\u003Ch3\u003EBias in Daten und Labels\u003C/h3\u003E\n\u003Cp\u003EHistorische Labels spiegeln wider, wie frühere Entscheidungen getroffen und gemessen wurden. Wenn einige Gruppen weniger Chancen, eine weniger vollständige Nachverfolgung oder andere Überprüfungsstandards erhielten, kann das resultierende Label diese Unterschiede kodieren.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/policy/data-ethics/data-bias/\"\u003EDaten-Bias\u003C/a\u003E kann auch durch Stichprobenziehung, fehlende Werte, Proxy-Variablen und die Wahl des Optimierungsziels entstehen. Die Abschwächung erfordert die Untersuchung, wie die Daten produziert wurden, die Evaluierung der Ergebnisse über relevante Gruppen hinweg und die Überprüfung, wie die Ausgaben des Modells die spätere Datenerfassung beeinflussen.\u003C/p\u003E\n\u003Ch3\u003EBegrenzte Interpretierbarkeit\u003C/h3\u003E\n\u003Cp\u003EModelle variieren darin, wie leicht ihre Mechanik inspiziert werden kann. Lineare Koeffizienten und kleine Entscheidungsbäume bieten relativ direkte Repräsentationen, während große Ensembles und neuronale Netze eine Vorhersage über viele interagierende Komponenten verteilen.\u003C/p\u003E\n\u003Cp\u003EPost-hoc-Erklärungsmethoden können den Feature-Einfluss abschätzen oder lokales Verhalten charakterisieren, aber diese Erklärungen haben ihre eigenen Annahmen und Einschränkungen. Das erforderliche Maß an Interpretierbarkeit hängt von der Entscheidung, der Zielgruppe und den geltenden Governance-Anforderungen ab.\u003C/p\u003E\n\u003Ch3\u003EKorrelation ohne Kausalität\u003C/h3\u003E\n\u003Cp\u003EPrädiktive Modelle nutzen oft Korrelationen aus, die Schätzungen verbessern, ohne den Mechanismus zu identifizieren, der sie hervorgebracht hat. Ein Feature kann stark prädiktiv sein, weil es als Proxy für eine andere Variable dient oder weil ein früherer Geschäftsprozess die Beziehung geschaffen hat.\u003C/p\u003E\n\u003Cp\u003EDies kann für einige Prognoseaufgaben ausreichend sein, ist es aber weniger, wenn das Unternehmen wissen möchte, welche Intervention das Ergebnis verändern wird. Kausale Fragen erfordern Studiendesigns und Annahmen, die über die standardmäßige prädiktive Modellierung hinausgehen.\u003C/p\u003E\n\u003Ch3\u003EWartungskosten\u003C/h3\u003E\n\u003Cp\u003EEin Modell ist mit Daten-Pipelines, Feature-Logik, Serving-Infrastruktur und Geschäftsdefinitionen verbunden. Änderungen an einer dieser Komponenten können sein Verhalten beeinflussen. Teams müssen den Trainingskontext beibehalten, Modellartefakte versionieren, das Produktionsverhalten überwachen und bestimmen, wann ein Modell untersucht, neu trainiert oder ausgemustert werden muss. Der betriebliche Aufwand wächst, wenn ein Unternehmen viele Modelle mit unterschiedlichen Eigentümer:innen, Abhängigkeiten und Aktualisierungsplänen unterhält.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_building-and-running-machine-learning-models-on-snowflake":{"id":"title-v2-dbc7a0bdf7","additionalClasses":"anchor-title anchor-title--building-and-running-machine-learning-models-on-snowflake","type":"heading2","lines":["Erstellen und Ausführen von Modellen für maschinelles Lernen auf Snowflake"],":type":"snowflake-site/components/title-v2"},"text_building-and-running-machine-learning-models-on-snowflake_0":{"id":"text-330a323f41","text":"\u003Cp\u003EDie Modellentwicklung stützt sich auf Datenaufbereitung, Experimente, Rechenressourcen, Versionsverwaltung und Inferenz. Wenn diese Phasen auf unzusammenhängende Umgebungen aufgeteilt sind, müssen Teams Datensätze und Feature-Logik reproduzieren, Artefakte zwischen Systemen verschieben und rekonstruieren, welche Eingaben eine bestimmte Modellversion hervorgebracht haben.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/product/features/end-to-end-ml-workflows/\"\u003ESnowflake ML\u003C/a\u003E unterstützt diese Phasen neben verwalteten Unternehmensdaten. Anwender:innen können Features aufbereiten, Modelle trainieren, Experimente vergleichen, Modellversionen registrieren, Inferenz ausführen und das Produktionsverhalten über Snowflake-Funktionen überwachen.\u003C/p\u003E\n\u003Cp\u003ESnowpark bietet DataFrame-APIs für die Abfrage und Verarbeitung von Daten in Snowflake mit Python, Java oder Scala. Für Python-Workflows für maschinelles Lernen können Anwender:innen Transformationen konstruieren und mit Snowflake-Daten arbeiten, ohne zuerst das komplette Trainingsset in ein separates Verarbeitungssystem zu exportieren.\u003C/p\u003E\n\u003Cp\u003EFür Workloads, die spezialisierte Bibliotheken, CPUs oder GPUs erfordern, bietet die Snowflake Container Runtime vorkonfigurierte, anpassbare Umgebungen für Experimente, Modelltraining, Hyperparameter-Anpassung und Batch-Inferenz. Die Umgebungen sind versioniert, sodass Teams einen Workload an eine bestimmte Runtime binden und Migrationen auf spätere Versionen planen können.\u003C/p\u003E\n\u003Cp\u003ESobald ein Modell trainiert ist, kann es in der Snowflake Model Registry mit seinen Metadaten und Versionen protokolliert werden. Die Registry unterstützt gängige Modelltypen, einschließlich scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, TensorFlow, Keras, MLflow-Modelle und Hugging Face-Pipelines, mit Unterstützung für benutzerdefinierte Modelltypen über die CustomModel-Schnittstelle.\u003C/p\u003E\n\u003Cp\u003ERegistrierte Modelle können Batch-Inferenz in einem virtuellen Warehouse von Snowflake ausführen oder Snowpark Container Services für Workloads verwenden, die andere Rechenressourcen-Eigenschaften erfordern. Snowflake unterstützt auch verwaltete Echtzeit-Inferenzdienste mit dedizierten HTTP-Endpunkten für interaktive Anwendungen mit geringer Latenz.\u003C/p\u003E\n\u003Cp\u003EDie Aufbewahrung von Modellversionen und Metadaten innerhalb der verwalteten Umgebung gibt Teams auch eine klarere Aufzeichnung darüber, was Vorhersagen liefert. Zugriffskontrollen können bestimmen, wer ein Modell verwaltet oder aufruft, während gespeicherte Metriken und Metadaten den Vergleich und das Lifecycle-Management unterstützen. Das Modell bleibt ein Teil des Produktionssystems, aber seine Beziehung zu Daten, Rechenressourcen und Governance ist leichter zu bewahren, wenn diese Komponenten über eine gemeinsame Plattform betrieben werden.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_selecting-models-with-confidence":{"id":"title-v2-a82af9a4b7","additionalClasses":"anchor-title anchor-title--selecting-models-with-confidence","type":"heading2","lines":["Modelle mit Zuversicht auswählen"],":type":"snowflake-site/components/title-v2"},"text_selecting-models-with-confidence_0":{"id":"text-ec0201b534","text":"\u003Cp\u003EModelle für maschinelles Lernen unterscheiden sich darin, wie sie Beziehungen darstellen, mit welchen Daten sie gut funktionieren und welche Kompromisse sie einführen. Diese Vielfalt ist Teil davon, wie sich maschinelles Lernen in Unternehmen in der Praxis entwickelt. Unternehmen benötigen die Flexibilität, verschiedene Familien zu evaluieren, das Modell auszuwählen, das zu jeder Aufgabe passt, und ein Portfolio zu unterstützen, das sich weiterentwickeln kann, wenn sich Daten, Anforderungen und verfügbare Methoden ändern.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_selecting-models-with-confidence_0":{"id":"text-4ef37f9cbf","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EWICHTIGSTE ERKENNTNIS\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EMaschinelles Lernen in Unternehmen ist selten um eine einzige Modellfamilie herum aufgebaut. 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