DictVectorizer
A scikit-learn tool for converting lists of dictionaries into a numeric feature matrix. It automatically applies one-hot encoding to string features and must be fit on the training set to avoid data leakage.
Timeline (last 90 days)
The formal model uses DictVectorizer for one-hot encoding (fit on the training set only), with logistic regression set to solver="liblinear" and C=1.0, giving a validation-set AUC of 0.732.
If DictVectorizer converts a list of dictionaries into a numeric matrix, it automatically one-hot encodes string features and keeps numeric features at their original values, and fit must be called only on the training set to avoid data leakage.
All Facts (2)
The formal model uses DictVectorizer for one-hot encoding (fit on the training set only), with logistic regression set to solver="liblinear" and C=1.0, giving a validation-set AUC of 0.732.
50%UnverifiedIf DictVectorizer converts a list of dictionaries into a numeric matrix, it automatically one-hot encodes string features and keeps numeric features at their original values, and fit must be called only on the training set to avoid data leakage.
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