[ Python ] Scikit-Learn, Numeric 표준화 / Category Onehot 하는 Pipeline 및 모델링하는 코드

numeric_features = ['age', 'fare']
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())])
## category 있는 경우 
categorical_features = ['embarked', 'sex', 'pclass']
categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))])

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

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

param_grid = {
    'preprocessor__num__imputer__strategy': ['mean', 'median'],
    'classifier__C': [0.1, 1.0, 10, 100],
}

#grid_search = GridSearchCV(clf, param_grid, cv=10, iid=False)
#grid_search.fit(X_train, y_train)

• https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html

Column Transformer with Mixed Types — scikit-learn 0.21.2 documentation