Bagging concept in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create multiple bootstrap samples for bagging.

ML Python

from sklearn.utils import resample

samples = [resample(data, replace=[1], n_samples=100) for _ in range(10)]

Drag options to blanks, or click blank then click option'

AFalse

B0

CNone

DTrue

Attempts:

3 left

💡 Hint

Common Mistakes

Using replace=False will create samples without replacement, which is not bagging.

Setting replace to None or 0 causes errors.

Bagging uses bootstrap samples with replacement, so replace=True is needed.

2fill in blank

medium

Complete the code to train multiple decision trees on bootstrap samples.

ML Python

from sklearn.tree import DecisionTreeClassifier

models = []
for sample in samples:
    clf = DecisionTreeClassifier()
    clf.fit(sample[[1]], sample[[2]])
    models.append(clf)

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Atarget

BX

Cy

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using the whole data instead of target for labels.

Confusing features and target variables.

Decision trees are trained using features X and target y.

3fill in blank

hard

Fix the error in the code to aggregate predictions from all models by majority vote.

ML Python

import numpy as np

predictions = np.array([model.predict(X_test) for model in models])
final_prediction = np.apply_along_axis(lambda x: np.bincount(x).[1](), axis=0, arr=predictions)

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Asum

Bargmax

Cmax

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using max returns the maximum count value, not the index.

Using sum or mean does not give the majority class.

np.bincount(x).argmax() returns the most common prediction (majority vote) among models.

4fill in blank

hard

Fill both blanks to create a bagging ensemble using scikit-learn's BaggingClassifier.

ML Python

from sklearn.ensemble import BaggingClassifier

bagging = BaggingClassifier(base_estimator=[1], n_estimators=10, random_state=42)
bagging.fit([2], y_train)

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ADecisionTreeClassifier()

BRandomForestClassifier()

CX_train

DX_test

Attempts:

3 left

💡 Hint

Common Mistakes

Using RandomForestClassifier as base estimator is incorrect.

Using test data X_test for training causes errors.

The base estimator for bagging is usually a decision tree, and training uses X_train features.

5fill in blank

hard

Fill all three blanks to evaluate bagging model accuracy on test data.

ML Python

from sklearn.metrics import [1]

preds = bagging.predict([2])
accuracy = [3](y_test, preds)
print(f"Accuracy: {accuracy:.2f}")

Drag options to blanks, or click blank then click option'

Aaccuracy_score

BX_test

Dconfusion_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Using confusion_matrix instead of accuracy_score for accuracy.

Predicting on training data instead of test data.

Use accuracy_score metric and X_test features to predict and evaluate accuracy.

Practice

1. What is the main idea behind bagging in machine learning?

easy

A. Training multiple models on random samples and combining their results

B. Using a single model with all data to avoid randomness

C. Reducing the number of features to simplify the model

D. Increasing the depth of a decision tree to improve accuracy

Solution

1. Step 1: Understand bagging concept

   Bagging stands for Bootstrap Aggregating, which means training many models on different random samples of the data.

2. Step 2: Identify the purpose of bagging

   It combines the results of these models to make predictions more stable and accurate.

3. Final Answer:

   Training multiple models on random samples and combining their results -> Option A

4. Quick Check:

   Bagging = multiple models + random samples + combine results [OK]

Hint: Bagging = many models + random data + combine predictions [OK]

Common Mistakes:

• Thinking bagging uses only one model
• Confusing bagging with feature selection
• Believing bagging increases model complexity by depth

2. Which of the following is the correct way to create a bagging classifier in Python using scikit-learn?

easy

A. BaggingClassifier(tree=DecisionTreeClassifier(), count=10)

B. BaggingClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=10)

C. BaggingClassifier(estimators=10, base=DecisionTree())

D. Bagging(base=DecisionTree(), estimators=10)

Solution

1. Step 1: Recall scikit-learn bagging syntax

   The correct class is BaggingClassifier, and it takes base_estimator and n_estimators as parameters.

2. Step 2: Match parameters to options

   BaggingClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=10) uses base_estimator=DecisionTreeClassifier() and n_estimators=10, which is correct syntax.

3. Final Answer:

   BaggingClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=10) -> Option B

4. Quick Check:

   BaggingClassifier + base_estimator + n_estimators = D [OK]

Hint: Use BaggingClassifier(base_estimator, n_estimators) in sklearn [OK]

Common Mistakes:

• Using wrong parameter names like 'base' or 'estimators'
• Confusing BaggingClassifier with Bagging
• Passing parameters in wrong order or with wrong names

3. Consider this Python code using bagging with decision trees:

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import BaggingClassifier

iris = load_iris()
X, y = iris.data, iris.target

bagging = BaggingClassifier(base_estimator=DecisionTreeClassifier(max_depth=2), n_estimators=5, random_state=42)
bagging.fit(X, y)
predictions = bagging.predict(X)
print(sum(predictions == y))

What does the printed number represent?

medium

A. Number of correct predictions on the training data

B. Number of incorrect predictions on the training data

C. Total number of samples in the dataset

D. Number of decision trees used in the bagging

Solution

1. Step 1: Understand the code output

   The code prints sum(predictions == y), which counts how many predicted labels match the true labels.

2. Step 2: Interpret the printed value meaning

   This count is the number of correct predictions on the training data.

3. Final Answer:

   Number of correct predictions on the training data -> Option A

4. Quick Check:

   sum(predictions == y) = correct predictions [OK]

Hint: sum(predictions == y) counts correct predictions [OK]

Common Mistakes:

• Thinking it counts incorrect predictions
• Confusing it with dataset size
• Assuming it prints number of trees

4. You wrote this code but get an error:

from sklearn.ensemble import BaggingClassifier
bagging = BaggingClassifier(base_estimator=DecisionTreeClassifier(), n_estimators='10')
bagging.fit(X_train, y_train)

What is the likely cause of the error?

medium

A. BaggingClassifier does not have a fit method

B. base_estimator must be a string, not a model instance

C. n_estimators should be an integer, not a string

D. DecisionTreeClassifier is not imported

Solution

1. Step 1: Check parameter types

   n_estimators expects an integer number of models, but '10' is a string.

2. Step 2: Identify error cause

   Passing a string instead of int causes a type error when fitting the model.

3. Final Answer:

   n_estimators should be an integer, not a string -> Option C

4. Quick Check:

   n_estimators must be int, not str [OK]

Hint: n_estimators must be int, not quoted string [OK]

Common Mistakes:

• Passing n_estimators as string instead of int
• Forgetting to import DecisionTreeClassifier
• Thinking base_estimator must be string

5. You want to improve a model's stability by using bagging with decision trees. Which approach is best to reduce overfitting while keeping good accuracy?

hard

A. Use many deep trees trained on the same full dataset without sampling

B. Use one very deep decision tree trained on all data

C. Use a single shallow tree with no bagging

D. Use many shallow decision trees trained on random samples and combine their votes

Solution

1. Step 1: Understand bagging effect on overfitting

   Bagging reduces overfitting by training many models on random samples and averaging results.

2. Step 2: Choose model depth and sampling

   Shallow trees reduce overfitting individually, and random sampling adds diversity, improving stability and accuracy.

3. Final Answer:

   Use many shallow decision trees trained on random samples and combine their votes -> Option D

4. Quick Check:

   Bagging + shallow trees + random samples = less overfitting [OK]

Hint: Bagging + shallow trees + random samples = stable, accurate model [OK]

Common Mistakes:

• Using one deep tree causes overfitting
• Training many deep trees on full data lacks diversity
• Ignoring bagging and using single tree