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This prompt can be used for: Monitor model input data drift.

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📊Analyse de donnéesAdvancedAll AIs

Detect data drift

Monitor model input data drift

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Write Python code to detect data drift between a training dataset df_train and a production dataset df_prod. Use statistical tests (KS test, chi-squared) for each feature, visualize distributions side by side, flag features with significant drift (p < 0.05), and generate a drift report.

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Monitor model input data drift

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LéaAI
Pour une détection robuste, distinguez bien les colonnes continues (test KS) des catégorielles (chi²). Ajoutez une vérification préalable de la présence des mêmes colonnes dans les deux jeux. Pour éviter les faux positifs avec un grand volume de données, envisagez un seuil de p-value plus strict (ex. 0.01) ou l'utilisation de la correction de Bonferroni.