如何确保GridSearchCV首先要分开，然后将其插入？

发布于 2025-02-05 05:41:02 字数 605 浏览 3 评论 0原文

我有一个GridSearchCV，带有一个看起来像这样的管道：

numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('scaler', StandardScaler())
])


preprocessor = ColumnTransformer(transformers=[
    ('num', numeric_transformer, numeric_features),
])

clf = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('classifier', LogisticRegression(solver='lbfgs'))
])

我的GridSearchCV看起来像这样：

search = GridSearchCV(clf, param_grid, cv = 5, scoring = "roc_auc",error_score=0.0)

使用交叉验证= 5

，我如何确保我先将数据拆分，然后最常见？

原文

I have a GridSearchCV, with a pipeline that looks something like this:

numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('scaler', StandardScaler())
])


preprocessor = ColumnTransformer(transformers=[
    ('num', numeric_transformer, numeric_features),
])

clf = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('classifier', LogisticRegression(solver='lbfgs'))
])

my GridSearchCV looks like this:

search = GridSearchCV(clf, param_grid, cv = 5, scoring = "roc_auc",error_score=0.0)

with Cross Validation = 5

So, how do I ensure that I split the data first, and then impute in the most frequent?

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酸甜透明夹心 2025-02-12 05:41:03

GridSearchCV将像这样大致运行：

for train_index, val_index in StratifiedKFold(n_splits=5).split(X, y):
    X_train, X_val = X[train_index], X[val_index]
    y_train, y_val = y[train_index], y[val_index]

    clf = Pipeline(steps=[
        ('preprocessor', preprocessor),
        ('classifier', LogisticRegression(solver='lbfgs'))
    ]) 

    clf.fit(X_train, y_train)
    clf.predict(X_val, y_val)

您可以确定simpleImputer and standardsCaler将做.fit（） and code>和.transform（）每个折叠。

GridSearchCV will run roughly like this:

for train_index, val_index in StratifiedKFold(n_splits=5).split(X, y):
    X_train, X_val = X[train_index], X[val_index]
    y_train, y_val = y[train_index], y[val_index]

    clf = Pipeline(steps=[
        ('preprocessor', preprocessor),
        ('classifier', LogisticRegression(solver='lbfgs'))
    ]) 

    clf.fit(X_train, y_train)
    clf.predict(X_val, y_val)

You can be sure that SimpleImputer and StandardScaler will do .fit() and .transform() for each fold.

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