Gradient Boosting (GBM) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the GradientBoostingClassifier from scikit-learn.

ML Python

from sklearn.ensemble import [1]

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ALogisticRegression

BRandomForestClassifier

CKNeighborsClassifier

DGradientBoostingClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a different classifier like RandomForestClassifier by mistake.

Confusing GradientBoostingClassifier with LogisticRegression.

The GradientBoostingClassifier is the correct class to import for gradient boosting classification tasks.

2fill in blank

medium

Complete the code to create a GradientBoostingClassifier with 100 trees.

ML Python

model = GradientBoostingClassifier(n_estimators=[1])

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A10

B100

C1000

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using too few trees like 1 or 10 which may underfit.

Using too many trees like 1000 which may be slow to train.

The n_estimators parameter sets the number of boosting stages or trees. 100 is a common default value.

3fill in blank

hard

Fix the error in the code to fit the model on training data X_train and y_train.

ML Python

model.[1](X_train, y_train)

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Afit

Bpredict

Ctransform

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit to train the model.

Using transform which is for data preprocessing.

The fit method trains the model on the given data.

4fill in blank

hard

Fill both blanks to create a dictionary of feature importances from the trained model.

ML Python

feature_importances = {feature: model.[1][index] for index, feature in enumerate([2])}

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Afeature_importances_

Bfeature_names

Cfeatures

Dcoef_

Attempts:

3 left

💡 Hint

Common Mistakes

Using coef_ which is for linear models, not gradient boosting.

Using features which is not a standard variable name.

The trained model stores feature importances in feature_importances_. The list of feature names is typically stored in feature_names.

5fill in blank

hard

Fill all three blanks to compute accuracy score of the model on test data.

ML Python

from sklearn.metrics import [1]
predictions = model.[2](X_test)
accuracy = [3](y_test, predictions)

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Aaccuracy_score

Bpredict

Dconfusion_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Using confusion_matrix instead of accuracy_score for metric.

Calling score method instead of using accuracy_score function.

We import accuracy_score to measure accuracy. We use predict to get model predictions. Then we compute accuracy by comparing true labels and predictions.