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28.4011 24H4.41235C2.31362 24 0.621094 22.2962 0.621094 20.2088V3.79124C0.621094 1.69251 2.3249 0 4.41235 0ZM28.4011 21.4612C29.1007 21.4612 29.6648 20.897 29.6648 20.1975V3.77995C29.6648 3.08037 29.1007 2.5162 28.4011 2.5162H4.41235C3.71277 2.5162 3.14859 3.08037 3.14859 3.77995V20.1975C3.14859 20.897 3.71277 21.4612 4.41235 21.4612H28.4011Z\" fill=\"#249EDC\"/\u003E\r\n\u003Cpath fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M29.6648 20.1975C29.6648 20.897 29.1007 21.4612 28.4011 21.4612H4.41235C3.71277 21.4612 3.14859 20.897 3.14859 20.1975V3.77995C3.14859 3.08037 3.71277 2.5162 4.41235 2.5162H28.4011C29.1007 2.5162 29.6648 3.08037 29.6648 3.77995V20.1975ZM27.7466 6.49927C28.04 6.17205 28.0174 5.66432 27.6902 5.35966V5.34837C27.363 5.055 26.8552 5.07755 26.5506 5.40477L18.7311 13.9577C18.686 14.0141 18.6296 14.0592 18.5731 14.1156C17.2981 15.3117 15.3009 15.2327 14.1162 13.9577L6.2854 5.39351C5.98075 5.05501 5.47299 5.03242 5.14577 5.33708C4.80727 5.64173 4.7847 6.1495 5.08935 6.47672L10.8327 12.7503L5.6084 17.4217C5.2699 17.7151 5.24733 18.2228 5.5407 18.5613C5.83407 18.8998 6.35311 18.9224 6.68033 18.629L11.9159 13.9464L12.9201 15.0409C13.8454 16.0564 15.0865 16.6093 16.418 16.6093C17.7495 16.6093 18.9906 16.0564 19.9159 15.0409L20.8637 14.0028L26.0428 18.6516H26.0654C26.4039 18.945 26.9117 18.9111 27.205 18.5726C27.4984 18.2341 27.4645 17.7264 27.126 17.433L21.9695 12.818L27.7466 6.49927Z\" fill=\"#249EDC\"/\u003E\r\n\u003C/svg\u003E\u003C/span\u003E\r\n\u003C/div\u003E","cssContent":".share-icon__svg *{pointer-events:none}.share-icon-group{display:flex;gap:24px;align-items:center}.share-icon{display:inline-block}.share-icon svg{height:32px}.share-icon__svg:hover path{fill:var(--ui-02);transition:300ms ease fill}.share-icon__svg path{transition:300ms ease fill}","jsContent":"const pageURL = window.location.href;\r\n\r\ndocument.addEventListener('click', (e) =\u003E {\r\n\r\nconst targetClassesArr = [...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"]}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/de/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-ca07bf5fc9","additionalClasses":"headline-decoration","type":"heading2","lines":["Übersicht"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-c471d9a933","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ERandom Forest ist einer der leistungsstärksten und beliebtesten Algorithmen zur Erstellung von \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ml-models/\"\u003EML-Modellen\u003C/a\u003E. Dieses überwachte Lernmodell erstellt mehrere Entscheidungsbäume und kombiniert dann Prognosen aus diesen Bäumen, um genauere und zuverlässigere Ergebnisse zu erzielen. Die Fähigkeit des Algorithmus, Probleme mit fehlenden oder verrauschten Daten zu umgehen, ist einer der Hauptgründe, warum er häufig für Anwendungen wie Kreditwürdigkeitsprüfung, Bedarfsprognose und Bildklassifizierung eingesetzt wird.\u003C/p\u003E\n\u003Cp\u003EIn diesem Leitfaden besprechen wir, wie der Random Forest funktioniert und warum er ein wichtiges Werkzeug für die Entwicklung zuverlässiger maschineller Lern- und \u003Ca href=\"https://www.snowflake.com/de/fundamentals/ai-models/\"\u003EKI-Modelle\u003C/a\u003E ist.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_":{"columnClassNames":{"container_115652453":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__480869401":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1444615495":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__222865509":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1413360324":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__455068363":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1500909857":"aem-GridColumn aem-GridColumn--default--12","container_1858830768":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1978652079":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"container_copy_copy_":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"a","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-f777f2f7e3","additionalClasses":"headline-decoration","type":"heading2","lines":["Was ist Random Forest?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-e4738f6ed4","text":"\u003Cp\u003ERandom Forest ist ein Ensemble-Algorithmus für maschinelles Lernen, der während seiner Trainingszeit viele Entscheidungsbäume erstellt. Jeder Baum wird auf einer zufälligen Teilmenge des gesamten Trainings-Datasets trainiert, wählt zufällig eine bestimmte Anzahl von Datenattributen aus jedem Entscheidungspunkt innerhalb des Baums aus und generiert dann eigene Prognosen. \u003C/p\u003E\n\u003Cp\u003EModelle, die mithilfe von Random Forest erstellt wurden, können sowohl für die Klassifizierung (bestimmt, welche Prognose von den meisten Bäumen ausgewählt wird) als auch \u003Ca href=\"https://www.snowflake.com/de/fundamentals/regression-models/\"\u003Efür die Regressionsanalyse\u003C/a\u003E (ein Durchschnitt der Prognosen aus allen Bäumen) verwendet werden. \u003C/p\u003E\n\u003Cp\u003EEin Modell, das E-Mail-Nachrichten als Spam oder nicht als Spam klassifiziert, analysiert beispielsweise die Ergebnisse aus allen Baumgruppen und wählt die Klassifizierung, die von der Mehrheit von ihnen gewählt wird. Im Gegensatz dazu würde ein Modell zur Vorhersage der Eigenheimpreise die Ergebnisse aller Bäume durchschnittlich berechnen.\u003C/p\u003E\n\u003Cp\u003EDiese Methode reduziert das Risiko, dass extreme Prognosen die Endergebnisse verzerren, und bietet einfache Möglichkeiten, die Konfidenz und Variabilität jeder Prognose zu messen. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__1444615495":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-fe71a1d1c6","additionalClasses":"headline-decoration","type":"heading2","lines":["Vergleich zwischen Random Forest und Entscheidungsbäumen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-170553f640","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EAuf ihrer grundlegendsten Ebene ist ein Random Forest ein Ensemble von Entscheidungsbäumen. Es gibt jedoch viele praktische Unterschiede zwischen der Funktionsweise dieser beiden Ansätze.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E1. Datasets\u003C/h3\u003E\n\u003Cp\u003EEin Entscheidungsbaum nutzt das gesamte Trainings-Dataset und berücksichtigt alle verfügbaren Features (Datenattribute wie Standort, Größe und Alter eines Heims) bei seinen Prognosen. Ein Random Forest erstellt mehrere Bäume aus diesem Dataset und wählt zufällig Merkmale aus jedem aus, um Ergebnisse zu generieren.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E2. Prognosemethodik\u003C/h3\u003E\n\u003Cp\u003EEntscheidungsbäume folgen einem geraden Weg und generieren eine einzige Prognose. Ein Random Forest erhält Vorhersagen von jedem Baum und generiert eine Gesamtvorhersage, indem die Ergebnisse gezählt oder gemittelt werden.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E3. Interpretierbarkeit\u003C/h3\u003E\n\u003Cp\u003EEntscheidungsbäume haben eine einfach zu erklärende Methode, um zu Prognosen zu gelangen. Ein Random Forest ist viel komplexer, was es schwieriger macht, individuelle Prognosen zu erklären.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E4. Rechenressourcen\u003C/h3\u003E\n\u003Cp\u003EEin Entscheidungsbaum ist viel einfacher, lässt sich schneller trainieren und benötigt deutlich weniger Rechen- und Speicherressourcen. Das Training mehrerer Bäume in einem Random Forest kann rechenaufwendig sein und längere Trainingszeiten erfordern.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E5. Performance\u003C/h3\u003E\n\u003Cp\u003EEntscheidungsbäume können sehr genau sein, sind aber auch anfällig für Überanpassung, was dazu führt, dass ein Modell weniger genaue Prognosen trifft, wenn es Daten außerhalb seines Trainingssets präsentiert. Entscheidungsbäume können auch stärker durch fehlende oder verrauschte Daten beeinflusst werden. Die Vorhersagen, die Random-Forest-Algorithmen generieren, gelten im Allgemeinen als genauer, stabiler und robuster.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__1978652079":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"benefits","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-3fd3df1be6","additionalClasses":"headline-decoration","type":"heading2","lines":["Beteiligte Schritte am Random-Forest-Algorithmus"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-18bf2c54dc","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ERandom Forest erstellt Hunderte von Entscheidungsbäumen, die jeweils aus verschiedenen zufälligen Stichproben von Trainingsdaten lernen und unterschiedliche Kombinationen von Datenmerkmalen berücksichtigen. Anschließend kombinieren sie all ihre Prognosen durch Abstimmung oder Mittelwertbildung, um ein genaueres und zuverlässigeres Ergebnis zu erzielen, als es ein einzelner Baum erreichen könnte.\u003C/p\u003E\n\u003Cp\u003EIm Folgenden haben wir die wichtigsten Schritte zusammengefasst, die der Random Forest von den Rohdaten bis zur endgültigen Prognose durchläuft:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E1. Vorbereitung der Daten\u003C/h3\u003E\n\u003Cp\u003EDer Algorithmus nimmt das ursprüngliche Trainings-Dataset und bereitet es für die Verarbeitung auf. Eventuell erforderliche Reinigung, Formatierung oder Vorverarbeitung sind in dieser Phase abgeschlossen.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E2. Datenproben \u003C/h3\u003E\n\u003Cp\u003ERandom Forest verwendet eine statistische Stichprobenmethode namens Bagging (auch bekannt als Bootstrap-Aggregation), um Datenpunkte zufällig für jeden Baum auszuwählen, wobei sich viele der gleichen Datenpunkte über mehrere Bäume hinweg wiederholen. So wird sichergestellt, dass jeder Baum eine etwas andere Version der Trainingsdaten sieht.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E3. Erstellung jedes Baumes \u003C/h3\u003E\n\u003Cp\u003EJeder Baum wird durch wiederholtes Aufteilen des Datasets erstellt, um neue Zweige zu erstellen. Wenn Sie beispielsweise einen Baum bauen, um vorherzusagen, ob jemand wahrscheinlich ein neues Auto kauft, teilt sich der Baum möglicherweise basierend darauf, ob sein Jahreseinkommen über oder unter 100.000 USD liegt, und wiederum davon, ob er älter als 30 Jahre ist. An jedem Entscheidungspunkt wählt der Algorithmus zufällig eine Untermenge verfügbarer Merkmale aus und wählt eine aus, die die klarste Trennung zwischen verschiedenen Ergebnissen schafft.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E4. Aufbau des Forest \u003C/h3\u003E\n\u003Cp\u003EDer Algorithmus wiederholt die Schritte 2 und 3 an beliebiger Stelle zwischen 100 und 1000 Mal, um eine Sammlung verschiedener Entscheidungsbäume zu erstellen. Jeder Baum lernt unterschiedliche Muster, weil er unterschiedliche Daten sieht und unterschiedliche Merkmale berücksichtigt.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E5. Individuelle Prognosen treffen\u003C/h3\u003E\n\u003Cp\u003EWenn neue Daten eintreffen, trifft jeder Baum im Forest selbstständig seine eigene Prognose, indem er seine gelernten Entscheidungsregeln befolgt. Dies führt zu mehreren separaten Prognosen für denselben Eingang.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E6. Abrechnung oder Durchschnitt \u003C/h3\u003E\n\u003Cp\u003EBei Klassifizierungsproblemen zählt der Algorithmus Stimmen aus allen Bäumen und wählt die Klasse mit den meisten Stimmen aus. Bei Regressionsproblemen wird der Durchschnitt aller Baumvorhersagen berechnet, um das Endergebnis zu erhalten.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E7. Bereitstellung des Endergebnisses\u003C/h3\u003E\n\u003Cp\u003EDer Algorithmus liefert die konsolidierte Vorhersage zusammen mit optionalen Konfidenzmessgrößen, die darauf basieren, wie viel Übereinstimmung zwischen den einzelnen Bäumen bestand.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__455068363":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-bbf80c7a39","additionalClasses":"headline-decoration","type":"heading2","lines":["Wichtige Vorteile des Random Forest-Modells"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-6871c2cedd","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEgal, ob für die Klassifizierung oder Random-Forest-Regression: Das Random-Forest-Modell liefert hervorragende Ergebnisse aus komplexen Datasets mit minimaler Anpassung. Hier einige der wichtigsten Vorteile, die Random Forest zu einem Algorithmus für Data Scientists machen:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EHohe Genauigkeit\u003C/h3\u003E\n\u003Cp\u003EDer Random Forest liefert konstant eine starke Prognose-Performance über verschiedene Datasets und Problemtypen hinweg. Die kollektive Entscheidung von Hunderten von Bäumen führt in der Regel zu genaueren Ergebnissen als die eines einzelnen Baumes.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EGeringes Risiko von Überpassungen\u003C/h3\u003E\n\u003Cp\u003EIm Gegensatz zu einzelnen Entscheidungsbäumen, die sich Trainingsdaten zu eng merken können, bietet der Random Forest natürlichen Schutz vor Überanpassung. Jeder Baum sieht unterschiedliche Daten und Merkmale, eliminiert individuellen Bias und Fehler und führt zu einer besseren Generalisierung, wenn neue Daten präsentiert werden.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EUnterstützung verschiedener Datentypen\u003C/h3\u003E\n\u003Cp\u003ERandom Forest arbeitet nahtlos mit gemischten Datentypen, einschließlich Zahlenwerten (wie Alter oder Einkommen) und kategorialen Variablen (wie Farbe oder Marke), ohne dass eine umfangreiche Vorverarbeitung erforderlich ist. Dies macht es zu einer guten Wahl für reale Datasets, die unübersichtliche Informationen in verschiedenen Formaten enthalten.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIdentifizierung wichtiger Datenvariablen\u003C/h3\u003E\n\u003Cp\u003EDer Algorithmus ordnet automatisch ein, welche Eingangsvariablen den größten Einfluss auf eine bestimmte Prognose hatten – eine Technik, die als Feature-Wichtigkeit bezeichnet wird. Data Scientists können so ihre Daten besser verstehen, wichtige Faktoren identifizieren und \u003Ca href=\"https://www.snowflake.com/de/fundamentals/data-modeling/\"\u003EModelle\u003C/a\u003E potenziell vereinfachen, indem sie sich auf die wichtigsten Variablen konzentrieren.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EKonsistente und zuverlässige Performance \u003C/h3\u003E\n\u003Cp\u003ERandom Forest ist äußerst resistent gegen Ausreißer, Rauschen und kleine Änderungen in den Trainingsdaten. Wo andere Algorithmen mit geringen Datenabweichungen drastisch andere Ergebnisse erzielen, behält der Random Forest eine gleichbleibende Performance bei und ist damit zuverlässig für Produktionsumgebungen.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EErfordert minimale Anpassung\u003C/h3\u003E\n\u003Cp\u003ERandom Forest funktioniert „out of the box“ mit Standardeinstellungen gut. So ist sie für Anwender:innen aller Erfahrungsstufen zugänglich und ermöglicht eine schnelle Entwicklung von Prototypen und Baseline-Modellen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__222865509":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-da0d6cc3c9","additionalClasses":"headline-decoration","type":"heading2","lines":["Wichtige Einschränkungen von Random Forest"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-cbdad2ca61","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EIm Folgenden haben wir die wichtigsten Nachteile und Einschränkungen der Verwendung des Random-Force-Modells zusammengefasst:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESchwierigere Interpretation der Ergebnisse \u003C/h3\u003E\n\u003Cp\u003EIm Gegensatz zu einem einzelnen Entscheidungsbaum, bei dem es einfach ist, den genauen Entscheidungspfad nachzuverfolgen, verwendet der Random Forest Hunderte von Bäumen, um eine endgültige Vorhersage zu treffen. Dies erschwert die Erklärung, warum eine bestimmte Prognose getroffen wurde, und schränkt ihre Verwendung in regulierten Branchen oder Situationen ein, die eine transparente Entscheidungsfindung erfordern.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EEs erfordert mehr Zeit\u003C/h3\u003E\n\u003Cp\u003EHunderte von Bäumen aufzubauen, dauert viel länger als ein einzelnes Modell zu trainieren. Wenn die Anzahl der Bäume wächst, nimmt die Prognosezeit proportional zu, was für Echtzeitanwendungen oder Umgebungen mit begrenzten Ressourcen problematisch sein kann.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EBei unausgewogenen Daten kann es zu schlechten Ergebnissen kommen\u003C/h3\u003E\n\u003Cp\u003EBei Datasets, bei denen eine Klasse deutlich häufiger vorkommt als andere (z. B. Spam-Filterung, bei der die Mehrheit der Nachrichten legitim ist), kann der Zufallsforest bei der Erkennung der seltenen Ausnahmen, bei denen Genauigkeit am wichtigsten ist, schlecht abschneiden.  \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EEs ist speicherintensiv\u003C/h3\u003E\n\u003Cp\u003ERandom Forest erfordert das Speichern aller einzelnen Bäume, was bei großen Datasets oder der Erstellung von Forests mit Hunderten von Bäumen zu einem Engpass werden kann. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EProbleme mit unübersichtlichen Daten \u003C/h3\u003E\n\u003Cp\u003ERandom Forest ist zwar generell gut, um eine Überanpassung zu vermeiden, kann aber dennoch Probleme beim Umgang mit extrem unübersichtlichen oder ungenauen Daten haben. Wenn in den Trainingsdaten dieselben Fehler auftreten, kann der Algorithmus diese Fehler als vertrauenswürdig ansehen, was zu weniger genauen Vorhersagen führt, wenn neue Daten präsentiert werden. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__1413360324":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"tools","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-e3f7cf399a","additionalClasses":"headline-decoration","type":"heading2","lines":["Reale Anwendungen von Random Forest"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-02a07196c2","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EIm Folgenden haben wir reale Anwendungen von Zufallswäldern in verschiedenen Branchen zusammengefasst:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EBetrugserkennung\u003C/h3\u003E\n\u003Cp\u003EBanken, Kreditkartenunternehmen und andere \u003Ca href=\"https://www.snowflake.com/de/solutions/industries/financial-services/\"\u003EFinanzdienstleistungsunternehmen\u003C/a\u003E nutzen den Random Forest, um verdächtige Transaktionen zu identifizieren, indem sie Ausgabenmuster, Transaktionsorte, Beträge und Zeitpunkte analysieren. Der Algorithmus kann ungewöhnliches Verhalten schnell kennzeichnen, wie z. B. Käufe im Ausland oder mehrere hochwertige Transaktionen über einen kurzen Zeitraum, und hilft so, \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003EFinanzbetrug\u003C/a\u003E in Echtzeit zu erkennen.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDiagnose von Krankheiten \u003C/h3\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/solutions/industries/healthcare-and-life-sciences/\"\u003EGesundheitsdienstleister\u003C/a\u003E nutzen Random Forest, um Krankheiten zu diagnostizieren, indem sie Patientensymptome, Laborergebnisse, Krankengeschichte und demografische Informationen analysieren. Krankenhäuser nutzen sie beispielsweise zur Vorhersage des Risikos einer Rückübernahme von Patient:innen oder zur \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/healthcare-and-life-sciences/care-delivery/\"\u003EErkennung von Frühsymptomen\u003C/a\u003E wie Diabetes oder Herzerkrankungen anhand mehrerer Gesundheitsindikatoren.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EAktienkurse prognostizieren \u003C/h3\u003E\n\u003Cp\u003EWertpapierfirmen und Handelsplattformen nutzen Random Forest, um Kursänderungen an der Börse zu prognostizieren, indem sie technische Indikatoren, Handelsvolumen, Marktstimmung und Wirtschaftsdaten analysieren. Obwohl Marktprognosen nach wie vor eine Herausforderung darstellen, hilft der Algorithmus, Muster auf Finanzmärkten zu erkennen und Händler bei fundierteren Kauf-/Verkaufsentscheidungen zu unterstützen.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ECustomer Churn Prediction for Plan and Network Utilisation\u003C/h3\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/solutions/industries/advertising-media-entertainment/\"\u003EStreamingdienste\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/de/solutions/industries/telecom/\"\u003ETelekommunikationsanbieter\u003C/a\u003E und \u003Ca href=\"https://www.snowflake.com/de/solutions/industries/technology/\"\u003ESoftwareanbieter\u003C/a\u003E nutzen Random Forest, um Kund:innen zu identifizieren, die kurz vor der Kündigung stehen. Durch die Analyse von Nutzungsmustern, Zahlungshistorie, Kundenserviceinteraktionen und demografischen Daten können Unternehmen proaktiv Angebote zur Bindung an gefährdete Kund:innen ausrichten.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EProduktempfehlung \u003C/h3\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/solutions/industries/retail-consumer-goods/\"\u003EOnline-Händler\u003C/a\u003E nutzen Random Forest, um Produktempfehlungen zu unterbreiten, indem sie Kaufhistorie, Browserverhalten und Produktähnlichkeiten analysieren. Der Algorithmus trägt zur Umsatzsteigerung bei, indem er relevante Produkte vorschlägt, die Kund:innen wahrscheinlich anhand von Mustern ähnlicher Benutzer:innen kaufen.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EKreditrisiken bewerten \u003C/h3\u003E\n\u003Cp\u003EBanken und kreditgebende Institute nutzen Random Forest, um Kreditanträge zu bewerten, indem sie Faktoren wie Kredithistorie, Einkommen, Beschäftigungsstatus und Schuldenquote analysieren. So können Kreditgeber genauere Entscheidungen darüber treffen, ob sie Darlehen genehmigen und welche Zinssätze sie verschiedenen Antragstellern anbieten wollen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"]},"container_1858830768":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"solutions","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{},":itemsOrder":[]},"container_115652453":{"columnClassNames":{"text_copy_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"last","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy_copy_c":{"id":"title-v2-a2b6b39151","additionalClasses":"headline-decoration","type":"heading2","lines":["Fazit"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_copy_copy_":{"id":"text-afeb3fee7d","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ERandom Forest ist ein vielseitiges und leistungsstarkes Tool zur Erstellung von Prognosen und liefert eine gleichbleibend hohe Genauigkeit für alle Anwendungen, von der Betrugserkennung und medizinischen Diagnose bis hin zum Spam-Filter. Durch die Verwendung mehrerer Entscheidungsbäume vermeidet Random Forest die meisten Probleme im Zusammenhang mit unübersichtlichen Daten und Überanpassung und ist damit eine grundlegende Technologie für die Entwicklung von ML-Modellen. Dank der Fähigkeit, verschiedene Datentypen zu verarbeiten und eine gute Leistung ohne umfassende Optimierung zu erzielen, sind sie für Benutzer aller Kenntnisstufen zugänglich. Da Daten immer komplexer werden, bleiben robuste Ensemblemethoden wie der Random Forest für Praktiker, die leistungsstarke KI-Systeme aufbauen wollen, unerlässlich.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy_copy_c","text_copy_copy_copy_"]},"container_copy_copy__1500909857":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"faq","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-627cc65fe4","additionalClasses":"headline-decoration","type":"heading2","lines":["Häufig gestellte Fragen zu Random Forest"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-168a24e700","additionalClasses":"list--blue-bullets","showDivider":false,"accordionItemsList":[{"title":"Warum heißt er „Random“ Forest?","richText":"\u003Cp\u003EDer „Zufall“ kommt aus zwei wichtigen Quellen: Jeder Baum wird mit einer zufällig ausgewählten Teilmenge Ihrer Daten trainiert, und jeder Baum berücksichtigt nur eine zufällige Handvoll Faktoren an jedem Entscheidungspunkt. Diese Zufälligkeit macht den Algorithmus leistungsstark, indem die Bäume gezwungen werden, verschiedene nützliche Muster zu finden, die sich gegenseitig ergänzen.\u003C/p\u003E\n"},{"title":"Wie unterscheidet sich Random Forest von einem normalen Entscheidungsbaum?","richText":"\u003Cp\u003EStellen Sie sich einen Entscheidungsbaum so vor, als würden Sie eine Person nach ihrer Meinung fragen, während ein Random Forest wie eine Umfrage in einem Raum von 100 Personen ist, von denen jede leicht unterschiedliche Informationen zu dem Problem bringt. Indem Sie all ihre Antworten durch Abstimmung oder Durchschnitt kombinieren, erhalten Sie eine viel zuverlässigere und genauere Prognose, als sich nur auf das Urteil einer Person zu verlassen.\u003C/p\u003E\n"},{"title":"Wann sollte ich Random Forest anstelle anderer Algorithmen verwenden?","richText":"\u003Cp\u003ERandom Forest ist ein ausgezeichneter Ausgangspunkt, wenn Sie eine hohe Genauigkeit wollen, ohne viel Zeit mit Einstellungen zu verbringen, insbesondere wenn Sie mit gemischten Datentypen arbeiten oder verstehen müssen, welche Faktoren am wichtigsten sind. Wenn Sie jedoch genau erklären müssen, warum jede Prognose getroffen wurde, sollten Sie stattdessen einfachere, interpretierbare Algorithmen in Erwägung ziehen.\u003C/p\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","simple_snowflake_acc"]},"container_copy_copy__480869401":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-ed528f4ced","additionalClasses":"headline-decoration","type":"heading2","lines":["Kunden, die Snowflake einsetzen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-7085df389a","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-71d96bc69b","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2":{"id":"card-v2-91692dc471","configurationStatus":{"configured":true,"message":""},"type":"content-card","title":{"id":"title","type":"heading4","lines":["IGS Energy nutzt KI und ML für optimierte Anomalieerkennung und weniger Komplexität bei Prognosen"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://www.snowflake.com/de/customers/all-customers/case-study/igs-energy/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Customer Story lesen"},"image":{"id":"image","lazyEnabled":true,"alt":"simon data logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--432d0ae1-84d1-49c2-a339-e0cadbcc3d5b/igs-energy-customer-card%25402x.jpg?quality=85&preferwebp=true","width":"1680","height":"720",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/card-v2","text":{"id":"text","text":"\u003Cp\u003EMit Snowflake kann IGS Energy Daten nutzen, um KI-/ML-Anwendungsfälle zu unterstützen – von kostengünstigeren Prognosemodellen bis hin zu präziserer Anomalieerkennung. So kommt das Unternehmen seinem Ziel näher, eine nachhaltige Zukunft für alle zu verwirklichen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical"}},":itemsOrder":["card_v2"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-f2fb2161be","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2_copy":{"id":"card-v2-d62c8f21b3","configurationStatus":{"configured":true,"message":""},"type":"content-card","title":{"id":"title","type":"heading4","lines":["WHOOP Improves AI/ML Financial Forecasting While Enhancing Members’ Experiences"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/whoop/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Customer Story lesen (Englisch)"},"image":{"id":"image","lazyEnabled":true,"alt":"penske logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--15863a57-5aeb-4400-b348-edd4aedb6520/whoop-customer-card%25402x.jpg?quality=85&preferwebp=true","width":"1680","height":"720",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/card-v2","text":{"id":"text","text":"\u003Cp\u003EMit Snowflake und Apache Iceberg konnten die Teams von WHOOP den Datenzugriff zentralisieren und gleichzeitig die Komplexität verringern, die Kosten senken und wichtige Prozesse verbessern.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical"}},":itemsOrder":["card_v2_copy"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["title_v2","flexible_column_cont"]},"container_copy_copy__373061683":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"resources","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-676cda8f5c","type":"heading2","lines":["Ressourcen zu maschinellem 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