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im maschinellen Lernen: Ein Deep Dive für Datenexpert:innen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-95ca83c94d","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EErfahren Sie, was ein Entscheidungsbaum ist und wie er funktioniert. Entdecken Sie Entscheidungsbaumtypen, Analysen, Beispiele und Best Practices für maschinelles Lernen und Planung.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"content_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-c245a0b377",":type":"snowflake-site/components/container",":items":{"content_chip":{"id":"content-chip-de91d8aff5","cta":{"id":"cta","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Biografie lesen"},"image":{"id":"image","height":"4000","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--b120fe53-f92c-4e35-b20b-d18cfc828d8c/will-heany-snowflake.jpg?quality=85&preferwebp=true","alt":"Will Heany","lazyEnabled":true,"isLcpImage":true,"width":"2667",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Will Heany","Content Manager, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip"],"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-209e7497d5",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-7c1a78263b","youtubeVideoId":"F34xlRoQ3eQ","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"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1398138236":{"id":"flexible-column-container-4bac77400d","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-c1a216de8a",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-d8dcf42eb4","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-a22ae29fbc","additionalClasses":"related-topics ","text":"\u003Cul\u003E\r\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\r\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\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/regression/\"\u003ERegressionsmodell\u003C/a\u003E\u003C/li\u003E\r\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\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-573f325fe5","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-71570266c6","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EENTSCHEIDUNGSBÄUME DEFINIERT\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EEntscheidungsbäume sind Modelle für maschinelles Lernen, die wie Flussdiagramme funktionieren und ein Problem in eine Reihe datengestützter Fragen aufteilen, die zu einer Prognose oder Entscheidung führen. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-9c9d1338dc","text":"\u003Cp\u003EGenau wie Menschen verschiedene Optionen in Betracht ziehen, bevor sie eine Entscheidung treffen, nutzen \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/\"\u003EModelle für maschinelles Lernen\u003C/a\u003E verschiedene Methoden, um eine Prognose oder Empfehlung abzugeben. Entscheidungsbäume sind eine beliebte Option im \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/\"\u003Emaschinellen Lernen\u003C/a\u003E (ML), da sie Probleme in einfache Schritte aufbrechen und die Ergebnisse leicht verständlich machen.\u003C/p\u003E\r\n\u003Cp\u003EEntscheidungsbäume werden häufig beim überwachten Lernen eingesetzt, bei dem Modelle aus Beispielen lernen, die bereits bekannte, richtige Antworten haben. Diese kümmern sich in der Regel um Klassifizierungsaufgaben wie die Erkennung von Spam-Mails und Regressionsaufgaben wie die Prognose des Energieverbrauchs eines Gebäudes. Was sie auszeichnet, ist die Art und Weise, wie der Denkprozess des Entscheidungsbaums betrachtet und interpretiert werden kann. Indem wir beobachten, wie sich ein „Zweig” in verschiedene Richtungen bewegt und dabei verschiedene datengestützte Fragen anspricht, wird deutlich, wie die Überlegungen eines Modells zu einem bestimmten Ergebnis geführt haben.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-a-decision-tree":{"id":"title-v2-6e3294eefa","additionalClasses":"anchor-title anchor-title--what-is-a-decision-tree","type":"heading2","lines":["Was ist ein Entscheidungsbaum?"],":type":"snowflake-site/components/title-v2"},"text_what-is-a-decision-tree_0":{"id":"text-fd786fc923","text":"\u003Cp\u003EEin Entscheidungsbaum ist ein prädiktives Modell, das zu einer Antwort gelangt, indem es eine Reihe einfacher Fragen zu den Daten stellt und die Möglichkeiten bei jedem Schritt eingrenzt, bis ein Ergebnis übrig bleibt. Sein charakteristisches Merkmal ist die Lesbarkeit: Der Weg zu jeder einzelnen Prognose kann als Kette einfacher Regeln nachvollzogen werden, was ihn zu einem der wenigen Modelle macht, die ein Laie ohne Hilfe überprüfen kann.\u003C/p\u003E\n\u003Cp\u003EEntscheidungsbäume funktionieren wie Flussdiagramme. Jeder Split stellt einen Entscheidungspunkt dar, der zu unterschiedlichen Ergebnissen führt. So können sowohl Menschen als auch Computer Optionen in Betracht ziehen, Möglichkeiten durchdenken und die Ergebnisse verstehen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-does-a-decision-tree-work":{"id":"title-v2-fae4cacaaf","additionalClasses":"anchor-title anchor-title--how-does-a-decision-tree-work","type":"heading2","lines":["Wie funktioniert ein Entscheidungsbaum?"],":type":"snowflake-site/components/title-v2"},"text_how_does_a_deci":{"id":"text-7337053c46","text":"\u003Cp\u003EEin Entscheidungsbaum teilt ein Problem in eine Reihe von Fragen auf. Jede Frage trägt dazu bei, Unsicherheiten zu reduzieren, bis die Antwort klar ist.\u003C/p\u003E\n\u003Cp\u003EDer Prozess beginnt an der Wurzel mit einer Frage, die auf Daten basiert. In diesem Schritt führt der Algorithmus eine Merkmalselektion durch, bei der die relevanteste Variable für die Aufteilung der Daten identifiziert wird. Jede Antwort führt zu einer anderen Frage, wiederum basierend auf der Funktion, die in dieser Phase am besten hilft, die Daten zu trennen. Der Baum setzt diesen Prozess fort, bis er einen Blattknoten erreicht, wo eine endgültige Prognose oder Entscheidung getroffen wird.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_decision-tree-essentials":{"id":"title-v2-9f557258ba","additionalClasses":"anchor-title anchor-title--decision-tree-essentials","type":"heading2","lines":["Wichtige Entscheidungsbaumelemente"],":type":"snowflake-site/components/title-v2"},"text_decision-tree-essentials_0":{"id":"text-55a674be90","text":"\u003Cp\u003EEntscheidungsbäume umfassen in der Regel vier Komponenten:\u003C/p\u003E\n\u003Ch3\u003EWurzelknoten\u003C/h3\u003E\n\u003Cp\u003EWie physische Bäume beginnt auch hier alles. Sie sind der erste Schritt im Denkprozess, bei dem ein ganzes Dataset mit Bezug zu einer oder mehreren Fragen zusammengeführt wird, bevor eine Aufspaltung vorgenommen wird.\u003C/p\u003E\n\u003Ch3\u003EZweige\u003C/h3\u003E\n\u003Cp\u003EZweige teilen Datasets anhand von Werten innerhalb der Daten auf. Kund:innen, die älter als 30 Jahre sind, können beispielsweise einen anderen Weg einschlagen als diejenigen unter 30. Der Entscheidungsbaum führt jede Gruppe zu ihrem eigenen Ergebnis.\u003C/p\u003E\n\u003Ch3\u003EInterne Knoten\u003C/h3\u003E\n\u003Cp\u003EInterne Knoten sind Entscheidungspunkte, an denen das Modell eine Frage über die Daten stellt, um sie auf eine Pfadführung zu lenken. Das Servicemodell eines Händlers könnte sich beispielsweise historische Kaufdaten ansehen und fragen: „Kauft Shopper A eher rote oder blaue Hemden?”\u003C/p\u003E\n\u003Ch3\u003EBlattknoten\u003C/h3\u003E\n\u003Cp\u003EBlattknoten sind die Endpunkte eines Entscheidungsbaums, an denen der Denkprozess anhält und das Modell ein Ergebnis liefert. Fortsetzung des Handelsbeispiels: Wenn die Einkaufshistorie eine Präferenz für rote Hemden anzeigt, kann der Entscheidungsbaum des ML-Modells zu einem Blattknoten führen, der das Modell auffordert, mehrere Optionen für neue rote Hemden zu empfehlen, die gekauft werden können.\u003C/p\u003E\n\u003Cp\u003EIn der Praxis erkunden Entscheidungsbäume mehrere Wege auf einmal. 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font-family=\"Lato, Arial, sans-serif\" font-size=\"36\" font-weight=\"700\" fill=\"#FFFFFF\"\u003EFolgt saisonalen Trends?\u003C/text\u003E\u003Crect x=\"110\" y=\"826\" width=\"414\" height=\"140\" rx=\"12\" fill=\"#C4E4F7\"/\u003E\u003Ctext x=\"138\" y=\"876\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" letter-spacing=\"0\" fill=\"#11567F\"\u003EBLATTKNOTEN\u003C/text\u003E\u003Ctext x=\"138\" y=\"926\" font-family=\"Lato, Arial, sans-serif\" font-size=\"30\" font-weight=\"700\" fill=\"#11253F\"\u003ETrendiges Rot empfehlen\u003C/text\u003E\u003Crect x=\"554\" y=\"826\" width=\"414\" height=\"140\" rx=\"12\" fill=\"#C4E4F7\"/\u003E\u003Ctext x=\"582\" y=\"876\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" letter-spacing=\"0\" fill=\"#11567F\"\u003EBLATTKNOTEN\u003C/text\u003E\u003Ctext x=\"582\" y=\"926\" font-family=\"Lato, Arial, sans-serif\" font-size=\"30\" font-weight=\"700\" fill=\"#11253F\"\u003ETrendiges Blau empfehlen\u003C/text\u003E\u003Cg fill=\"none\" stroke=\"#5B9FD4\" stroke-width=\"4\" stroke-linecap=\"round\"\u003E\u003Cpath d=\"M0 1016 H56\"/\u003E\u003C/g\u003E\u003Ctext x=\"70\" y=\"1026\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" letter-spacing=\"0\" fill=\"#11567F\"\u003EZWEIG\u003C/text\u003E\u003Ctext x=\"196\" y=\"1026\" font-family=\"Lato, Arial, sans-serif\" font-size=\"27\" font-style=\"italic\" fill=\"#6B7680\"\u003Eteilt das Dataset nach einem Wert auf\u003C/text\u003E\u003C/svg\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_types-of-decision-trees":{"id":"title-v2-8d5231d36e","additionalClasses":"anchor-title anchor-title--types-of-decision-trees","type":"heading2","lines":["Arten von Entscheidungsbäumen"],":type":"snowflake-site/components/title-v2"},"text_types-of-decision-trees_0":{"id":"text-7e316e682e","text":"\u003Cp\u003EEs stehen zahlreiche gängige Entscheidungsbaum-Algorithmen zur Verfügung, von denen die meisten auf Klassifizierungs- und Regressionsaufgaben angewendet werden können. was Folgendes umfasst:\u003C/p\u003E\n\u003Ch3\u003ECART (Classification and Regression Trees)\u003C/h3\u003E\n\u003Cp\u003ECART ist ein weit verbreiteter Algorithmus, der sich von anderen Entscheidungsbaummethoden abhebt, da er für jedes Feature immer binäre Aufteilungen (ja/nein) erzeugt, wobei der Fokus auf der Aufteilung liegt, die die Werte in den Daten am besten trennt. Ein CART-Modell, das vorhersagt, ob ein Darlehen genehmigt werden sollte, könnte beispielsweise die Antragsteller zunächst nach „Einkommen \u003E 50.000 USD” (Ja/Nein) aufteilen und dann jede Gruppe anhand anderer Faktoren aufteilen, z. B. ob die Kreditwürdigkeit des Antragstellers über 750 liegt und ob der Antragsteller beschäftigt ist. Dies ist der Algorithmus hinter den meisten Produktionsbaum-Implementierungen, einschließlich \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-ml/reference/latest/api/modeling/snowflake.ml.modeling.tree.DecisionTreeClassifier\"\u003EDecisionTreeClassifier in Snowflake ML\u003C/a\u003E.\u003C/p\u003E\n\u003Ch3\u003EID3 (Iterativer Dichotomizer 3)\u003C/h3\u003E\n\u003Cp\u003EAls einer der ersten beliebten Entscheidungsbaum-Algorithmen teilt ID3 Daten in kleinere Gruppen auf, indem es Fragen auswählt, die mögliche Antworten eingrenzen, bis sie eine gewünschte Vorhersage oder Empfehlung erreichen. Ein Spam-Filter kann beispielsweise E-Mails mit dem Wort „Angebot“ hervorheben, da dieses Wort häufig in kommerziellen Werbeanzeigen verwendet wird.\u003C/p\u003E\n\u003Ch3\u003EC4.5\u003C/h3\u003E\n\u003Cp\u003EC4.5 erstellt Entscheidungsbäume, indem es eine Reihe von Ja- oder Nein-Fragen stellt, die Daten in kleinere Gruppen aufteilen und so präzisere Prognosen ermöglichen. Es ist eine Verbesserung gegenüber ID3, da es sowohl kategoriale Werte (wie „Spam” oder „kein Spam”) als auch numerische Werte (wie „Alter” oder „Einkommen”) verarbeitet und gleichzeitig Lücken wie fehlende Daten umgeht. So könnte beispielsweise ein Telekommunikationsunternehmen mit C4.5 Faktoren wie Alter, Standort und Datennutzung abwägen, um spezifische Tarifoptionen für einen Kunden zusammenzustellen, selbst wenn die Informationen über den potenziellen Kunden unvollständig sind.\u003C/p\u003E\n\u003Ch3\u003ECHAID (Chi-Square Automatic Interaction Detection)\u003C/h3\u003E\n\u003Cp\u003ECHAID nutzt statistische Tests, um zu entscheiden, wo aufgeteilt werden soll, und erstellt oft Zweige mit mehreren Optionen auf einmal. So könnte beispielsweise ein Händler Kund:innen in Altersgruppen einteilen, wie z. B. Teenager, junge Erwachsene, Menschen mittleren Alters und Senioren, um vorherzusagen, welche demografische Gruppe am ehesten auf ein neues Treueprogramm reagieren wird.\u003C/p\u003E\n\u003Ch3\u003EBedingte Inferenzbäume\u003C/h3\u003E\n\u003Cp\u003EBedingte Inferenzbäume reduzieren den Bias, indem getestet wird, ob eine Variable relevant genug ist, um eine Spaltung zu rechtfertigen. So unterscheiden sie sich von regulären Entscheidungsbäumen wie ID3 und CART, die die Daten Schritt für Schritt aufspalten, ohne zu testen, ob ein Faktor statistisch signifikant ist. Ein regulärer Entscheidungsbaum könnte beispielsweise das Merkmal „besuchte Universität” bevorzugen, während ein bedingter Inferenzbaum dieses als statistisch irrelevant für die Vorhersage der Performance verwerfen könnte.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_decision-tree-splitting-criteria":{"id":"title-v2-680bc7cb08","additionalClasses":"anchor-title anchor-title--decision-tree-splitting-criteria","type":"heading2","lines":["Kriterien für die Aufteilung von Entscheidungsbäumen"],":type":"snowflake-site/components/title-v2"},"text_decision-tree-splitting-criteria_0":{"id":"text-0a703a46a5","text":"\u003Cp\u003EBei der Aufteilung von Daten verwenden ML-Modelle in der Regel eines von zwei gemeinsamen Entscheidungsbaumkriterien: Gini-Unreinheit oder Entropie. Jeder misst, wie gemischt die Daten sind, und der Algorithmus wendet seine gewählte Methode an, um die Aufteilung zu finden, die die Daten am effektivsten trennt.\u003C/p\u003E\n\u003Ch3\u003EGini-Unreinheit\u003C/h3\u003E\n\u003Cp\u003EGini untersucht, wie gut eine Frage die Daten in klare Gruppen unterteilt. Mathematisch spiegelt sie die Wahrscheinlichkeit wider, dass ein zufälliges Element falsch klassifiziert wird, wenn es entsprechend der Verteilung der Gruppe gekennzeichnet wird. Der CART-Algorithmus wendet diese Maßnahme an, um verschiedene Splits zu testen, und wählt diejenige aus, die die sauberste Trennung bewirkt. Indem Menschen gefragt werden, ob sie müde sind, entstehen zwei Gruppen: diejenigen, die wahrscheinlich Kaffee trinken, und diejenigen, die es nicht sind.\u003C/p\u003E\n\u003Ch3\u003EEntropie\u003C/h3\u003E\n\u003Cp\u003EEntropie misst die Unsicherheit im Dataset. Algorithmen wie ID3 und C4.5 nutzen Entropie, um den Informationsgewinn zu berechnen. Dieser stellt die Verringerung der Unsicherheit dar, die durch eine Spaltung entsteht. Der Baum wählt die Aufteilung aus, die Unsicherheit am meisten reduziert und so die klarste Trennung zwischen Klassen schafft. Die Frage, ob es Morgen oder Nachmittag ist, reduziert die Unsicherheit, weil sie Menschen in klarere Gruppen trennt, die die Entscheidung leiten.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-are-decision-trees-used-for":{"id":"title-v2-e11862dcba","additionalClasses":"anchor-title anchor-title--what-are-decision-trees-used-for","type":"heading2","lines":["Wofür werden Entscheidungsbäume verwendet?"],":type":"snowflake-site/components/title-v2"},"text_what-are-decision-trees-used-for_0":{"id":"text-d116c54127","text":"\u003Cp\u003EBeim maschinellen Lernen helfen Entscheidungsbäume Modellen dabei, Rohdaten in nützliche Erkenntnisse zu verwandeln. Dies ist insbesondere in Branchen hilfreich, in denen Entscheidungen gut unterstützt und zuverlässig sein müssen.\u003C/p\u003E\n\u003Cp\u003EHier einige häufige Verwendungen für Entscheidungsbäume:\u003C/p\u003E\n\u003Ch3\u003EGeschäftsstrategie und -planung\u003C/h3\u003E\n\u003Cp\u003EML-Modelle, die mit Entscheidungsbäumen trainiert wurden, eignen sich für Prognosen zu Umsatzwachstum, Preistrends, Kundenabwanderung, Lieferkettennachfrage und Lagerbeständen.\u003C/p\u003E\n\u003Ch3\u003ERisikobewertung und Risikominderung\u003C/h3\u003E\n\u003Cp\u003EIn Finanzen und Versicherungen helfen Entscheidungsbäume bei der Bewertung von Risiken wie Zahlungsausfällen, Ansprüchen oder anderen Verlusten. Indem sie sich verzweigenden Pfaden von Kundendaten folgen, wie z. B. Kredithistorien, Einkommensniveaus oder Schadenmustern, helfen sie Aktuaren, Underwritern und Finanzanalysten bei der Bereitstellung präziserer Risikoschätzungen. Ein Beispiel hierfür finden Sie unter \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-predicting-insurance-claims-regression-model/\"\u003EVorhersage von Versicherungsansprüchen mit einem XGBoost-Regressionsmodell\u003C/a\u003E.\u003C/p\u003E\n\u003Ch3\u003EKundensegmentierung und Targeting\u003C/h3\u003E\n\u003Cp\u003EMarketingexpert:innen können Entscheidungsbaummodelle nutzen, um \u003Ca href=\"https://www.snowflake.com/de/fundamentals/customer-segmentation/\"\u003EKunden\u003C/a\u003E anhand von Kaufverhalten, Demografie und Onlineaktivitäten in Gruppen aufzuteilen. So können Unternehmen personalisiertere Angebote bereitstellen und vorhersagen, welche Kund:innen am ehesten auf Kampagnen reagieren. Snowflake veröffentlicht einen \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-e2e-customer-targeting-with-snowflake-ml/\"\u003EEnd-to-End-Walkthrough für das Customer Targeting\u003C/a\u003E, der diesem Muster folgt.\u003C/p\u003E\n\u003Ch3\u003EMedizinische Diagnose und Behandlung\u003C/h3\u003E\n\u003Cp\u003EML-Modelle im Gesundheitswesen basieren oft auf Entscheidungsbäumen, um Patientendaten zu interpretieren. Ein Modell kann beispielsweise Symptome abwägen, Testergebnisse berücksichtigen und Familienanamnesen untersuchen, um wichtige Informationen für Diagnosen und Behandlungen zu sammeln. Ein \u003Ca href=\"https://www.snowflake.com/en/developers/guides/healthcare-ml-breast-cancer-classification/\"\u003ELeitfaden zur Brustkrebsklassifizierung mit XGBoost\u003C/a\u003E zeigt, wie ein baumbasiertes Ensemble auf genau diese Art von Diagnosedaten angewendet wird.\u003C/p\u003E\n\u003Ch3\u003EErkennung von Finanzbetrug\u003C/h3\u003E\n\u003Cp\u003EBanken und andere risikoscheue Finanzinstitute können mit Entscheidungsbaummodellen verdächtige Aktivitäten erkennen. Durch die Analyse von Mustern wie Kaufgrößen und Retouren können Modelle Transaktionen identifizieren, die auf potenziellen Betrug, Geldwäsche oder andere potenziell kriminelle Aktivitäten hinweisen. Die \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003ELösung zur Betrugserkennung und Bekämpfung von Finanzkriminalität\u003C/a\u003E von Snowflake behandelt, wie diese Modelle in großem Maßstab auf Transaktionsdaten angewendet werden.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_advantages-of-decision-trees":{"id":"title-v2-f059a91652","additionalClasses":"anchor-title anchor-title--advantages-of-decision-trees","type":"heading2","lines":["Vorteile von Entscheidungsbäumen"],":type":"snowflake-site/components/title-v2"},"text_advantages-of-decision-trees_0":{"id":"text-670b2eb40a","text":"\u003Cp\u003EEntscheidungsbäume vereinfachen ansonsten zeitaufwendige Denkprozesse und liefern schneller und effizienter Ergebnisse. Hier einige spezifische Vorteile:\u003C/p\u003E\r\n\u003Ch3\u003EEinfache Interpretierbarkeit\u003C/h3\u003E\r\n\u003Cp\u003EDie Transparenz von Entscheidungsbäumen hilft dabei, das Geheimnis aus dem Denkprozess von ML zu nehmen. Jeder kann visuell der Schritt-für-Schritt-Logik folgen, die das Modell zu seinen Schlussfolgerungen und Empfehlungen geführt hat.\u003C/p\u003E\r\n\u003Cp\u003EDa ein trainierter Baum als eine Reihe von für Menschen lesbaren Regeln gelesen werden kann, wird er oft als interpretierbare Alternative zu Modellen mit höherer Genauigkeit, aber undurchsichtigen Modellen wie \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/neural-network/\" target=\"_blank\"\u003Eneuronalen Netzen\u003C/a\u003E gewählt. In regulierten Umgebungen, in denen eine Entscheidung erklärt und nicht nur durch ihre Genauigkeit gerechtfertigt werden muss, kann diese Eigenschaft wichtiger sein als ein paar Punkte an prädiktivem Lift – weshalb Bäume überall dort üblich bleiben, wo \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/ai-governance/ai-transparency/\"\u003EKI-Transparenz\u003C/a\u003E eine Anforderung ist.\u003C/p\u003E\r\n\u003Ch3\u003EGeringerer Aufwand bei der Datenaufbereitung\u003C/h3\u003E\r\n\u003Cp\u003EEntscheidungsbäume können sowohl Kategorien als auch Zahlenwerte verarbeiten, sodass Analyst:innen weniger Zeit mit der Konvertierung oder Umformatierung von Daten verbringen müssen. Sie reduzieren den Aufwand für die Vorbereitung vor dem Ausführen von Modellen.\u003C/p\u003E\r\n\u003Ch3\u003EHochflexibel\u003C/h3\u003E\r\n\u003Cp\u003EEntscheidungsbäume können sich an verschiedene Probleme anpassen, da es sich jeweils um ein in sich geschlossenes Modell handelt, das unabhängig voneinander Prognosen treffen kann. Dank dieses flexiblen Designs können viele Bäume kombiniert und ihre Ausgaben aggregiert werden, sodass sie größere und komplexere Aufgaben bewältigen können.\u003C/p\u003E\r\n\u003Ch3\u003EBehebt fehlende Werte\u003C/h3\u003E\r\n\u003Cp\u003EIm Gegensatz zu einigen Modellen, die vollständige Datasets erfordern, können Entscheidungsbäume funktionieren, wenn Informationen fehlen. Und das erreichen sie, indem sie unvollständigen Datensätzen geringere Gewichtungen zuweisen oder indem sie Daten auf mehrere mögliche Pfade aufteilen.\u003C/p\u003E\r\n\u003Ch3\u003EFunktioniert gut mit kleinen Datasets\u003C/h3\u003E\r\n\u003Cp\u003EEntscheidungsbäume können ohne riesige Datenmengen nützliche Muster finden. Sie sind selbst dann effektiv, wenn Informationen begrenzt sind, und deshalb wertvoll, wenn die Erfassung von Daten zu rar oder zu zeitaufwendig ist.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_decision-tree-limitations":{"id":"title-v2-5e214c61fd","additionalClasses":"anchor-title anchor-title--decision-tree-limitations","type":"heading2","lines":["Einschränkungen des Entscheidungsbaums"],":type":"snowflake-site/components/title-v2"},"text_decision-tree-limitations_0":{"id":"text-4eb1cdbdd4","text":"\u003Cp\u003EEntscheidungsbäume haben trotz ihrer Vorteile immer noch ihre Nachteile. Hier einige der häufigsten Probleme, mit denen Menschen und Maschinen bei der Verwendung von Entscheidungsbäumen zur Begründung konfrontiert werden:\u003C/p\u003E\r\n\u003Ch3\u003EAnfällig für Überanpassung\u003C/h3\u003E\r\n\u003Cp\u003EEntscheidungsbäume können zu detailliert werden, da sie sich auf Eigenheiten in den Trainingsdaten stützen, anstatt allgemeine Muster zu lernen. Das Ergebnis kann ein Modell sein, das im Training genau aussieht, aber mit neuen, unsichtbaren Daten zu kämpfen hat.\u003C/p\u003E\r\n\u003Ch3\u003ERauschen / verrauscht\u003C/h3\u003E\r\n\u003Cp\u003EEntscheidungsbäume können durch zufällige oder irrelevante Variationen in einem Dataset ausgelöst werden, die keine wahren Muster widerspiegeln. Selbst kleine Geräuschmengen können dazu führen, dass sich der Baum irreführend spaltet, was zu instabilen Prognosen führt.\u003C/p\u003E\r\n\u003Ch3\u003EKann zu Bias-Splits führen\u003C/h3\u003E\r\n\u003Cp\u003EWenn ein bestimmtes Feature ein Dataset dominiert, kann ein Entscheidungsbaum dieses manchmal auf Kosten anderer, gleich oder wichtiger Faktoren überindexieren. Wenn ein medizinisches Modell beispielsweise mehr Wert auf die Postleitzahl von Patient:innen legt als auf Faktoren wie Ernährung oder Lebensstil, kann das zu ungenauen Prognosen, Empfehlungen und Diagnosen führen.\u003C/p\u003E\r\n\u003Ch3\u003EWeniger genau als Ensemblemethoden\u003C/h3\u003E\r\n\u003Cp\u003EEinzelne Entscheidungsbäume treffen Entscheidungen selbst, was zu Fehlern oder Überanpassungen führen kann. \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/ensemble-learning/\"\u003EEnsemblemethoden\u003C/a\u003E hingegen kombinieren Ergebnisse aus mehreren Bäumen. Dieser kollektive Ansatz liefert in der Regel genauere, umfassendere und einheitlichere Ergebnisse.\u003C/p\u003E\r\n\u003Cp\u003EDer Entscheidungsbaum ist der Basis-Lerner in den bekanntesten Ensemble-Familien, sodass die beiden Ansätze nicht wirklich Konkurrenten sind. Ein \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/random-forest/\"\u003ERandom Forest\u003C/a\u003E trainiert viele Bäume parallel auf verschiedenen Teilmengen der Daten und Features und mittelt dann deren Ausgaben, um die Varianz eines einzelnen Baums auszugleichen. \u003Ca href=\"https://www.snowflake.com/de/fundamentals/what-is-gradient-boosting/\"\u003EGradient Boosting\u003C/a\u003E verwendet denselben Basis-Lerner, baut die Bäume jedoch sequenziell auf, wobei jeder neue Baum die Fehler der vorherigen korrigiert. Die Ensemble-Struktur unterscheidet sich; der zugrunde liegende Baum jedoch nicht.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_decision-tree-limitations_0":{"id":"text-579acd96e7","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cb\u003EHÄUFIGER FALLSTRICK\u003C/b\u003E \u003C/p\u003E\n\u003Cp\u003EÜberanpassung ist der Fehlermodus hinter den meisten der oben genannten Einschränkungen – ein unbeschnittener Baum, der sich Eigenheiten in den Trainingsdaten gemerkt hat, wird auch ungewöhnlich empfindlich gegenüber Rauschen und ungewöhnlich zuversichtlich in Bezug auf ein dominantes Feature wirken. Behandeln Sie Pruning und Validierung an ungesehenen Daten als Standardpraxis und nicht als optionale Anpassung.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_decision-tree-best-practices":{"id":"title-v2-17d6c27438","additionalClasses":"anchor-title anchor-title--decision-tree-best-practices","type":"heading2","lines":["Best Practices für Entscheidungsbäume"],":type":"snowflake-site/components/title-v2"},"text_decision-tree-best-practices_0":{"id":"text-dcb94f2289","text":"\u003Cp\u003EUnternehmen können die Effektivität ihrer ML-Entscheidungsbäume maximieren, indem sie folgende praktische Tipps befolgen:\u003C/p\u003E\n\u003Ch3\u003EStarke Funktionen auswählen\u003C/h3\u003E\n\u003Cp\u003EBetonen Sie Faktoren, die die größte Datentrennung bieten, wie z. B. die Transaktionsgröße bei der Betrugserkennung oder Testergebnisse bei der medizinischen Diagnose. Funktionen mit hoher Vorhersagekraft können Entscheidungsbäumen helfen, klarere Ergebnisse zu erzielen und unnötige Spaltungen zu vermeiden. Die Verwaltung dieser Features in einem \u003Ca href=\"https://www.snowflake.com/en/developers/guides/intro-to-feature-store/\"\u003EFeature Store\u003C/a\u003E hält dieselben Definitionen zwischen Training und Produktion konsistent.\u003C/p\u003E\n\u003Ch3\u003EPruning, um Überanpassung zu vermeiden\u003C/h3\u003E\n\u003Cp\u003EEbenso wie ein Baumpfleger Äste schneidet, um das Wachstum eines Baumes zu managen und abgestorbene Blätter wegzuräumen, ist es wichtig, unnötige Entscheidungsbaumäste zu schneiden. Pruning ist der Schlüssel, um zu verhindern, dass sich ein Baum auf Trainingsdaten fixiert und stattdessen nach Mustern sucht, die zu aussagekräftigen Ergebnissen führen können.\u003C/p\u003E\n\u003Ch3\u003EValidierung mit neuen Daten\u003C/h3\u003E\n\u003Cp\u003EUm einen Baum ehrlich zu halten, überprüfen Sie seine Performance, indem Sie ihn Daten aussetzen, die er noch nicht gesehen hat. So können Überanpassungen vermieden werden. Eine Testaufspaltung zurückzuhalten und die Ergebnisse mit standardmäßigen Metriken zur \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/model-evaluation/\"\u003EModellbewertung\u003C/a\u003E zu überprüfen, ist die übliche Methode, um zu bestätigen, dass ein Baum generalisiert statt sich speichert.\u003C/p\u003E\n\u003Ch3\u003ESplits überwachen\u003C/h3\u003E\n\u003Cp\u003EViele ML-Bibliotheken bieten Tools, um Features zu sortieren, von denen der Baum am meisten abhängig ist, und um aufzuzeigen, wie Spaltungen vorgenommen werden. Anhand dieser Kontrollen und Abwägungen lässt sich leichter erkennen, wie ein Modell Daten, Gründe und Ergebnisse verarbeitet. Sobald ein Baum in Produktion ist, gehört dieselbe Transparenz in das fortlaufende \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/observability/model-monitoring/\"\u003EModell-Monitoring\u003C/a\u003E, sodass eine Verschiebung der Features, die die Splits antreiben, erkannt wird, bevor sie sich in den Prognosen zeigt.\u003C/p\u003E\n\u003Cp\u003EEntscheidungsbäume sind beim maschinellen Lernen beliebt, weil sie einfach, übersichtlich und flexibel sind. Sie eignen sich für viele Geschäftsaufgaben, wie z. B. die Beurteilung von Kreditrisiken, die Prognose von Verkäufen oder die Gruppierung von Kunden für das Marketing. 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