CatBoost in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the CatBoostClassifier from the catboost library.

ML Python

from catboost import [1]

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ARandomForestClassifier

BCatBoostClassifier

CSVC

DKNeighborsClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Importing other classifiers like RandomForestClassifier instead of CatBoostClassifier.

Misspelling the class name.

The CatBoostClassifier is the main class used for classification tasks in CatBoost.

2fill in blank

medium

Complete the code to create a CatBoostClassifier model with 100 trees.

ML Python

model = CatBoostClassifier([1]=100)

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Adepth

Brandom_seed

Citerations

Dlearning_rate

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'learning_rate' to set number of trees instead of 'iterations'.

Confusing 'depth' with number of trees.

The 'iterations' parameter sets the number of trees (boosting rounds) in CatBoost.

3fill in blank

hard

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

ML Python

model.fit([1], y_train)

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AX_train

BX_test

Cy_train

Dmodel

Attempts:

3 left

💡 Hint

Common Mistakes

Passing y_train as the first argument instead of X_train.

Passing test data instead of training data.

The fit method requires the features (X_train) as the first argument and the target (y_train) as the second.

4fill in blank

hard

Fill both blanks to create predictions and calculate accuracy score using sklearn.

ML Python

from sklearn.metrics import {{BLANK_1 }}
predictions = model.predict(X_test)
accuracy = {{BLANK_2}}(y_test, predictions)

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Aaccuracy_score

Bmean_squared_error

Cclassification_report

Dconfusion_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean_squared_error which is for regression, not classification.

Using confusion_matrix which returns a matrix, not a score.

We import accuracy_score to measure how many predictions match the true labels. Then we use accuracy_score to calculate accuracy.

5fill in blank

hard

Fill all three blanks to create a CatBoostClassifier with depth 6, learning rate 0.1, and random seed 42.

ML Python

model = CatBoostClassifier(depth=[1], learning_rate=[2], random_seed=[3])

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

A6

B0.1

C42

D100

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up learning rate and depth values.

Using random_seed values outside typical range.

The 'depth' controls tree depth, 'learning_rate' controls step size, and 'random_seed' ensures reproducibility.