XGBoost in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the XGBoost classifier.

ML Python

from xgboost import [1]

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AXGBClassifier

BXGBRegressor

CXGBModel

DXGBTree

Attempts:

3 left

💡 Hint

Common Mistakes

Using XGBRegressor which is for regression tasks.

Trying to import a non-existent class like XGBTree.

The XGBClassifier is the class used for classification tasks in XGBoost.

2fill in blank

medium

Complete the code to create an XGBoost classifier with 100 trees.

ML Python

model = XGBClassifier(n_estimators=[1])

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A50

B100

C1000

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using too few trees like 10 which may underfit.

Using too many trees like 1000 which may be slow.

The n_estimators parameter sets the number of trees. 100 is a common choice.

3fill in blank

hard

Fix the error in the code to train the model on features X and labels y.

ML Python

model.fit([1], y)

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Amodel

Bfeatures

CX

Dy

Attempts:

3 left

💡 Hint

Common Mistakes

Passing labels y as the first argument instead of features.

Passing the model itself or an undefined variable.

The fit method requires the feature data X as the first argument.

4fill in blank

hard

Fill both blanks to predict labels and calculate accuracy score.

ML Python

preds = model.[1](X_test)
accuracy = [2](y_test, preds)

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Apredict

Baccuracy_score

Cfit

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit instead of predict to get predictions.

Using score method instead of accuracy_score function.

Use predict to get predictions and accuracy_score to measure accuracy.

5fill in blank

hard

Fill all three blanks to create a dictionary of feature importances for features in feature_names.

ML Python

importances = { [1]: model.feature_importances_[i] for i, [2] in enumerate([3]) }

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Afeature

Bname

Cfeature_names

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping variable names in the loop.

Using wrong variable for the list of feature names.

Use name as the key, feature as the loop variable, and feature_names as the list of features.