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Summary - Regularization

In this group you learned L1, L2, and ElasticNet regularization.

Summary: regularisation

In this group:

  • Regularisation adds a penalty on coefficient size to reduce overfitting; features must be scaled first.
  • Lasso (L1): pushes coefficients to zero, selects features; may pick one of correlated features arbitrarily.
  • Ridge (L2): shrinks coefficients, shares the weight, is stable.
  • ElasticNet: a mix of both, l1_ratio.
  • alpha is chosen with CV: RidgeCV, LassoCV, ElasticNetCV; in logistic regression and SVMs C = 1/α.

Practice Compare the three models

Compare LinearRegression, RidgeCV(alphas=np.logspace(-2, 3, 11)) and LassoCV(cv=5, random_state=0) on the scaled data by their mean 5-fold CV R². Print the three scores with round(..., 3) on one line.

Starter code

model.py
import numpy as np
from sklearn.preprocessing import StandardScaler

rng = np.random.default_rng(0)
X = rng.normal(size=(60, 20))
y = 3 * X[:, 0] - 2 * X[:, 1] + 1.5 * X[:, 2] + rng.normal(0, 1, 60)
Xs = StandardScaler().fit_transform(X)
from sklearn.linear_model import LinearRegression, RidgeCV, LassoCV
from sklearn.model_selection import cross_val_score

# buraya yaz

Sample solution

model.py
import numpy as np
from sklearn.preprocessing import StandardScaler

rng = np.random.default_rng(0)
X = rng.normal(size=(60, 20))
y = 3 * X[:, 0] - 2 * X[:, 1] + 1.5 * X[:, 2] + rng.normal(0, 1, 60)
Xs = StandardScaler().fit_transform(X)
from sklearn.linear_model import LinearRegression, RidgeCV, LassoCV
from sklearn.model_selection import cross_val_score

modeller = [LinearRegression(), RidgeCV(alphas=np.logspace(-2, 3, 11)), LassoCV(cv=5, random_state=0)]
print(*[round(cross_val_score(m, Xs, y, cv=5).mean(), 3) for m in modeller])

Expected output

0.848 0.854 0.888

Questions and answers

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

1 If you want an interpretable model with few features, which do you choose?

  • No regularisation
  • Lasso Correct answer
  • Ridge
  • Ordinary regression

Explanation: Lasso's zero coefficients amount to feature selection.

2 Apply a strong penalty in logistic regression.

Question 
m = LogisticRegression(____=0.01)
Answer 
m = LogisticRegression(C=0.01)

Explanation: C is the inverse of the penalty strength; small C is a strong penalty.

3 Match the models.

Term Meaning
Lasso L1, sparse
Ridge L2, shared
ElasticNet L1 + L2
RidgeCV Chooses alpha

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