Random forest in depth in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

ML Python

from sklearn.ensemble import [1]

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ASVC

BDecisionTreeClassifier

CKNeighborsClassifier

DRandomForestClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Importing DecisionTreeClassifier instead of RandomForestClassifier

Importing unrelated classifiers like KNeighborsClassifier or SVC

The RandomForestClassifier is the correct class to import for building a random forest model for classification tasks.

2fill in blank

medium

Complete the code to create a random forest model with 100 trees.

ML Python

model = RandomForestClassifier(n_estimators=[1])

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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 can be slow

The n_estimators parameter sets the number of trees in the forest. 100 is a common choice for good performance.

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. Using predict or transform here would cause errors.

4fill in blank

hard

Fill both blanks to create a dictionary of feature importances and sort it by importance descending.

ML Python

importances = dict(enumerate(model.[1]))
sorted_importances = dict(sorted(importances.items(), key=lambda item: item[[2]], reverse=True))

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Afeature_importances_

B0

C1

Dfeatures_

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'features_' which does not exist

Sorting by item[0] which sorts by feature names, not importance

feature_importances_ is the attribute holding importance scores. Sorting by item[1] sorts by the importance value.

5fill in blank

hard

Fill all three blanks to predict on test data X_test, calculate accuracy, and print it.

ML Python

predictions = model.[1](X_test)
accuracy = [2](y_test, predictions)
print('Accuracy:', [3])

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Apredict

Baccuracy_score

Caccuracy

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using model.score instead of accuracy_score function

Printing the function name instead of the accuracy variable

Use predict to get predictions, accuracy_score to calculate accuracy, and print the accuracy variable.