Gradient Boosting for regression in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

ML Python

from sklearn.ensemble import [1]

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

ARandomForestRegressor

BGradientBoostingRegressor

CLinearRegression

DKNeighborsRegressor

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a classifier instead of a regressor.

Importing a different ensemble model like RandomForestRegressor.

The GradientBoostingRegressor is the correct class to import for gradient boosting regression models in scikit-learn.

2fill in blank

medium

Complete the code to create a Gradient Boosting regressor with 100 trees.

ML Python

model = GradientBoostingRegressor(n_estimators=[1])

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

A100

B10

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.

Setting n_estimators=100 creates a model with 100 boosting stages (trees), which is a common default.

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)

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

Afit

Bpredict

Ctransform

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Calling predict before fitting the model.

Using transform which is for data preprocessing.

The fit method trains the model on the given data. Using predict or transform here is incorrect.

4fill in blank

hard

Fill both blanks to compute the mean squared error between true and predicted values.

ML Python

from sklearn.metrics import [1]
error = [2](y_true, y_pred)

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

Amean_squared_error

Baccuracy_score

Cmean_absolute_error

Dr2_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using accuracy_score which is for classification.

Using mean_absolute_error which measures absolute errors, not squared.

The mean_squared_error function computes the average squared difference between true and predicted values, which is a common regression metric.

5fill in blank

hard

Fill all three blanks to create a Gradient Boosting regressor with learning rate 0.1, max depth 3, and fit it on data.

ML Python

model = GradientBoostingRegressor(learning_rate=[1], max_depth=[2])
model.[3](X_train, y_train)

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

A0.1

B3

Cfit

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit to train the model.

Setting max_depth too high or learning_rate too large.

Setting learning_rate=0.1 controls the step size at each boosting iteration. max_depth=3 limits tree depth to avoid overfitting. The fit method trains the model.