CodeBlanks Logo Icon CodeBlanks
Login Register

AdaBoost

AdaBoost increases the weight of misclassified samples for the next model.

AdaBoost

AdaBoost increases the weight of misclassified examples in every round; the next weak learner pays more attention to these "hard" examples. Each learner's vote is weighted by how accurate it is:

ada = AdaBoostClassifier(
    estimator=DecisionTreeClassifier(max_depth=1),
    n_estimators=200, learning_rate=0.5, random_state=0)
ada.fit(X_train, y_train)
print(ada.estimator_weights_[:5])   # learner vote weights

Because of the reweighting, AdaBoost is sensitive to label noise and outliers: a mislabelled example gets more and more attention.

Error and vote weight

In binary AdaBoost, a learner with error ε has the vote weight α = ½·ln((1-ε)/ε). If ε = 0.5 (random), α = 0: the vote has no effect. As the error drops, the vote weight grows:

import numpy as np
for e in [0.45, 0.3, 0.1]:
    print(e, round(0.5 * np.log((1 - e) / e), 3))

That's why it is enough for the weak learner to be slightly better than random; its contribution is adjusted accordingly.

Practice Vote weights

For the error rates [0.45, 0.3, 0.1], compute the vote weight with 0.5 * np.log((1 - e) / e) and print it with np.round(..., 3).

Starter code

model.py
import numpy as np

e = np.array([0.45, 0.3, 0.1])

# buraya yaz

Sample solution

model.py
import numpy as np

e = np.array([0.45, 0.3, 0.1])

alfa = 0.5 * np.log((1 - e) / e)
print(np.round(alfa, 3))

Expected output

[0.1   0.424 1.099]

Questions and answers

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

1 In AdaBoost, what happens to a misclassified example in the next round?

  • Its weight increases Correct answer
  • It is removed from the data
  • Its weight decreases
  • Its label is changed

Explanation: The next learner is pushed harder to classify this example correctly.

2 Give a stump as the weak learner.

Question 
ada = AdaBoostClassifier(____=DecisionTreeClassifier(max_depth=1))
Answer 
ada = AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1))

Explanation: The estimator parameter is the model copied in every round.

3 Match the concepts.

Term Meaning
sample weight Grows on hard examples
α Learner's vote weight
ε = 0.5 Vote weight 0
label noise AdaBoost's weakness

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