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Classical machine learning with scikit-learn. Use when building classification, regression, clustering models, or implementing feature engineering pipelines.
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| name | scikit-learn |
| description | Classical machine learning with scikit-learn. Use when building classification, regression, clustering models, or implementing feature engineering pipelines. |
Activate this skill when working with classical ML algorithms.
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score
preprocessor = ColumnTransformer([
("num", StandardScaler(), numeric_features),
("cat", OneHotEncoder(handle_unknown="ignore"), categorical_features),
])
pipeline = Pipeline([
("preprocessor", preprocessor),
("classifier", GradientBoostingClassifier(n_estimators=200)),
])
scores = cross_val_score(pipeline, X, y, cv=5, scoring="f1_macro")
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