CodeBlanks Logo Icon CodeBlanks
Login Register

SelectKBest

SelectKBest selects the top k features based on a statistical test.

SelectKBest

SelectKBest scores each feature one by one on its relationship with the target and keeps the best k. The scoring function is chosen per problem:

from sklearn.feature_selection import SelectKBest, f_classif, mutual_info_classif
sec = SelectKBest(score_func=f_classif, k=6).fit(X, y)
print(sec.get_support(indices=True))
print(sec.scores_.round(1))

f_classif (ANOVA F) measures linear separation, mutual_info_classif any kind of dependence. For regression f_regression and mutual_info_regression are used.

Pipeline and choosing k

Because SelectKBest looks at the target, using it outside a pipeline creates serious leakage (remember the random-data example in the pipeline lesson). k is a hyperparameter too:

pipe = make_pipeline(StandardScaler(), SelectKBest(f_classif), LogisticRegression())
gs = GridSearchCV(pipe, {"selectkbest__k": [2, 4, 6, 10, 20]}, cv=5).fit(X, y)
print(gs.best_params_)

That way the selection in each fold is made only with that fold's training data.

Practice The best 6 features

Train SelectKBest(f_classif, k=6) on the synthetic data; print the selected column indices. Then print how many of the selected ones are really among the first 6 columns.

Starter code

model.py
import numpy as np
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=400, n_features=20, n_informative=4, n_redundant=2, shuffle=False, random_state=0)
from sklearn.feature_selection import SelectKBest, f_classif

# buraya yaz

Sample solution

model.py
import numpy as np
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=400, n_features=20, n_informative=4, n_redundant=2, shuffle=False, random_state=0)
from sklearn.feature_selection import SelectKBest, f_classif

sec = SelectKBest(f_classif, k=6).fit(X, y)
idx = sec.get_support(indices=True)
print(idx)
print((idx < 6).sum())

Expected output

[ 1  2  4  5 15 19]
4

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 What is the weakness of univariate filters?

  • They can miss features that carry information only together Correct answer
  • They are very slow
  • They don't look at the target
  • They don't work for classification

Explanation: Each feature is scored separately; interactions like XOR are invisible.

2 Score with mutual information.

Question 
sec = SelectKBest(score_func=____, k=5)
Answer 
sec = SelectKBest(score_func=mutual_info_classif, k=5)

Explanation: Mutual information also captures non-linear dependence.

3 Match the scoring functions.

Term Meaning
f_classif ANOVA F, classification
f_regression Correlation-based, regression
mutual_info_classif Any dependence
chi2 Non-negative counts

All lessons in Intermediate

1 scikit-learn basics

  1. What is scikit-learn?
  2. Estimator API
  3. The fit method
  4. The predict method
  5. The score method
  6. Summary - scikit-learn

2 Pipeline

  1. What is a Pipeline?
  2. Steps
  3. fit and predict
  4. Advantages
  5. ColumnTransformer
  6. Summary - Pipeline

3 Cross-validation

  1. What is cross-validation?
  2. K-fold
  3. cross_val_score
  4. Stratified
  5. Advantages
  6. Summary - Cross-validation

4 Grid search

  1. What is grid search?
  2. GridSearchCV
  3. param_grid
  4. best_params_
  5. best_score_
  6. Summary - Grid search

5 SVM

  1. What is SVM?
  2. Margin
  3. Kernel
  4. C parameter
  5. Classification
  6. Summary - SVM

6 Project: SVM

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

7 Random forest

  1. What is random forest?
  2. Ensemble
  3. n_estimators
  4. Bagging
  5. Feature importance
  6. Summary - Random forest

8 Boosting

  1. What is boosting?
  2. AdaBoost
  3. Gradient boosting
  4. Learning rate
  5. Advantages
  6. Summary - Boosting

9 Clustering

  1. What is clustering?
  2. KMeans
  3. Centroid
  4. Elbow method
  5. Labels
  6. Summary - Clustering

10 KMeans in depth

  1. KMeans algorithm
  2. Initialization
  3. n_init
  4. inertia_
  5. random_state
  6. Summary - KMeans

11 Hierarchical clustering

  1. What is hierarchical clustering?
  2. Dendrogram
  3. Agglomerative
  4. Linkage
  5. Advantages
  6. Summary - Hierarchical clustering

12 Project: Clustering

  1. Preparing data
  2. Training KMeans
  3. Label analysis
  4. Printing results

13 PCA

  1. What is PCA?
  2. Variance
  3. n_components
  4. fit_transform
  5. explained_variance_
  6. Summary - PCA

14 Dimensionality reduction

  1. What is dimensionality reduction?
  2. Why reduce?
  3. PCA vs t-SNE
  4. Visualization
  5. Advantages
  6. Summary - Dimensionality reduction

15 Feature selection

  1. What is feature selection?
  2. Variance threshold
  3. SelectKBest
  4. Recursive elimination
  5. Feature importance
  6. Summary - Feature selection

16 Imbalanced data

  1. What is imbalanced data?
  2. SMOTE
  3. Class weight
  4. Oversampling
  5. Metric selection
  6. Summary - Imbalanced data

17 Project: Features

  1. Feature extraction
  2. Feature selection
  3. Training model
  4. Evaluation

18 Ensemble methods

  1. What is ensemble?
  2. Voting
  3. Bagging
  4. Boosting
  5. Stacking
  6. Summary - Ensemble

19 Regularization

  1. What is regularization?
  2. L1 (Lasso)
  3. L2 (Ridge)
  4. alpha
  5. ElasticNet
  6. Summary - Regularization

20 Intermediate final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4