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Feature Selection Methods in scikit-learn

Reference for feature-selection-analyzer · 107 lines

AI & Machine Learning / Classical Machine Learningv1.0.0Updated OCT 25 20264 references2 scripts

All selectors below are transformers: put them in a Pipeline after preprocessing so they are fitted on training folds only.

Filter

python
from sklearn.feature_selection import (VarianceThreshold, SelectKBest, SelectPercentile, f_classif,
                                       mutual_info_classif, mutual_info_regression, chi2)

VarianceThreshold(threshold=0.0)                       # constant columns (after scaling, pick a small threshold)
SelectKBest(score_func=mutual_info_classif, k=30)      # non-linear univariate dependence; set random_state via partial
SelectPercentile(score_func=f_classif, percentile=20)  # linear univariate (ANOVA F)
SelectKBest(chi2, k=50)                                # non-negative features (counts, TF-IDF)
python
from functools import partial
mi = partial(mutual_info_classif, random_state=0, discrete_features="auto")
selector = SelectKBest(mi, k=30)

Embedded: L1 and elastic net

python
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LogisticRegression, LassoCV, ElasticNetCV

l1 = SelectFromModel(LogisticRegression(penalty="l1", solver="saga", C=0.1, max_iter=5000))
lasso = SelectFromModel(LassoCV(cv=5, random_state=0))                         # regression
enet = SelectFromModel(ElasticNetCV(l1_ratio=[0.2, 0.5, 0.8], cv=5, random_state=0))
  • Scale features first (the penalty is unit-dependent).
  • L1 keeps one arbitrary member of a correlated group; elastic net spreads weight over the group (more stable).
  • Inspect the regularisation path (lasso_path, l1_min_c) to see the order in which features enter.

Embedded: tree importance

python
from sklearn.ensemble import RandomForestClassifier
SelectFromModel(RandomForestClassifier(n_estimators=500, random_state=0), threshold="median")

Impurity-based importance (feature_importances_) is computed on training data and biased toward high-cardinality and continuous features; use permutation importance on held-out data for decisions.

Wrapper: RFE / RFECV

python
from sklearn.feature_selection import RFECV
from sklearn.model_selection import StratifiedKFold
rfecv = RFECV(estimator=LogisticRegression(max_iter=2000), step=0.1, cv=StratifiedKFold(5),
              scoring="average_precision", min_features_to_select=5, n_jobs=-1)
rfecv.fit(X_train_prepped, y_train)
print(rfecv.n_features_, rfecv.cv_results_["mean_test_score"])

step as a fraction removes that share of features per round (much faster on wide data).

Wrapper: sequential selection

python
from sklearn.feature_selection import SequentialFeatureSelector
sfs = SequentialFeatureSelector(model, n_features_to_select="auto", tol=0.001, direction="forward",
                                scoring="average_precision", cv=5, n_jobs=-1)

Costs O(features²) model fits; use on tens, not thousands, of features.

Permutation importance (held-out)

python
from sklearn.inspection import permutation_importance
r = permutation_importance(fitted_pipe, X_valid, y_valid, scoring="average_precision", n_repeats=20,
                           random_state=0, n_jobs=-1)
order = r.importances_mean.argsort()[::-1]
for i in order[:20]:
    print(f"{X_valid.columns[i]:<25} {r.importances_mean[i]:.4f} ± {r.importances_std[i]:.4f}")

Features whose mean minus 2 std is <= 0 are not distinguishable from zero on this validation set.

Boruta-style shadow features

python
import numpy as np
rng = np.random.default_rng(0)
shadow = X_train.apply(lambda c: rng.permutation(c.values)).add_prefix("shadow_")
X_aug = pd.concat([X_train, shadow], axis=1)
# fit a tree model on X_aug; a real feature must beat the best shadow importance (repeat many times)

The boruta package (BorutaPy) implements the full iterative test.

Stability selection

python
import numpy as np
from sklearn.utils import resample

def stability_selection(X, y, make_selector, n_runs=50, frac=0.5, seed=0):
    rng = np.random.default_rng(seed)
    counts = np.zeros(X.shape[1])
    for _ in range(n_runs):
        idx = rng.choice(len(X), int(frac * len(X)), replace=False)
        sel = make_selector().fit(X.iloc[idx], y.iloc[idx])
        counts += sel.get_support()
    return counts / n_runs          # keep features with frequency >= 0.6-0.8

Write each run's importances in long format (run, feature, importance) and feed them to importance_stability.py.

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