How can I create advanced pipelines in scikit-learn?

How can I create advanced pipelines in scikit-learn?

You can create advanced pipelines in scikit-learn using the Pipeline and FeatureUnion classes. Would you like a code example?

Yes, please provide a code example.

Here's a simple example:

from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer

numeric_features = ['age', 'salary']
categorical_features = ['gender', 'city']

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

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)
    ])

pipeline = Pipeline(steps=[('preprocessor', preprocessor),
                           ('classifier', SomeClassifier())])

What is the purpose of using FeatureUnion?

FeatureUnion allows you to combine multiple feature extraction methods into a single feature set, which can improve model performance.