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Variance threshold

Variance threshold removes low-variance features.

VarianceThreshold

The simplest filter: dropping columns whose variance is below a threshold. Constant or near-constant columns give no information to any model:

from sklearn.feature_selection import VarianceThreshold
sec = VarianceThreshold(threshold=0.01)
X2 = sec.fit_transform(X)
print(sec.get_support())   # which columns were kept

Variance depends on scale; that's why the threshold is applied before scaling (on scaled data all variances are 1). For 0/1 columns the variance is p(1-p).

get_support

All scikit-learn selectors give a boolean mask with get_support() and the indices of the kept columns with get_support(indices=True). They are used inside pipelines, and names are tracked with get_feature_names_out:

print(sec.get_support(indices=True))
print(sec.variances_.round(3))

The variance threshold doesn't look at the target; that's why it can't separate noise columns, only drop constants.

Practice Drop constant columns

Apply VarianceThreshold(threshold=0) to the digit data. Print the number of remaining columns and the indices of the dropped columns (with ~sec.get_support()) on separate lines.

Starter code

model.py
import numpy as np
from sklearn.datasets import load_digits
from sklearn.feature_selection import VarianceThreshold

X, y = load_digits(return_X_y=True)

# buraya yaz

Sample solution

model.py
import numpy as np
from sklearn.datasets import load_digits
from sklearn.feature_selection import VarianceThreshold

X, y = load_digits(return_X_y=True)

sec = VarianceThreshold(threshold=0)
X2 = sec.fit_transform(X)
print(X2.shape[1])
print(np.where(~sec.get_support())[0])

Expected output

61
[ 0 32 39]

Questions and answers

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

1 Roughly what is the variance of a 0/1 column whose values are 99% zeros?

  • 0
  • 0.0099 Correct answer
  • 0.99
  • 0.5

Explanation: p(1-p) = 0.01 · 0.99 ≈ 0.0099.

2 Get the indices of the kept columns.

Question 
idx = sec.get_support(____=True)
Answer 
idx = sec.get_support(indices=True)

Explanation: indices=True returns an index array instead of a mask.

3 Match the concepts.

Term Meaning
VarianceThreshold Drops constants
get_support() Boolean mask
variances_ Column variances
p(1-p) 0/1 column variance

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