Random forest classifier 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]

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

ADecisionTreeClassifier

BKNeighborsClassifier

CRandomForestClassifier

DSVC

Attempts:

3 left

💡 Hint

Common Mistakes

Importing DecisionTreeClassifier instead of RandomForestClassifier

Importing from sklearn.tree instead of sklearn.ensemble

The RandomForestClassifier is imported from sklearn.ensemble to build a random forest model.

2fill in blank

medium

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

ML Python

model = RandomForestClassifier(n_estimators=[1])

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

A10

B200

C50

D100

Attempts:

3 left

💡 Hint

Common Mistakes

Using too few trees like 10 which may underfit

Using too many trees unnecessarily increasing computation

Setting n_estimators=100 creates a random forest with 100 decision trees.

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

Btransform

Cpredict

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit to train the model

Using transform which is for preprocessing

The fit() method trains the model on the given data.

4fill in blank

hard

Fill both blanks to predict labels for X_test and calculate accuracy score.

ML Python

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

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

Apredict

Bpredictions

Cfit

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit instead of predict for predictions

Passing model instead of predictions to accuracy_score

Use predict() to get predictions, then pass predictions to accuracy_score to compute accuracy.

5fill in blank

hard

Fill all three blanks to create a random forest with max depth 5, fit it, and predict.

ML Python

model = RandomForestClassifier(n_estimators=[1], max_depth=[2])
model.[3](X_train, y_train)
preds = model.predict(X_test)

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

A100

B5

Cfit

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using max_depth=10 instead of 5

Calling predict instead of fit to train

Set n_estimators=100, max_depth=5, then call fit() to train the model.

Practice

1. What is the main idea behind a Random Forest Classifier?

easy

A. It clusters data without labels.

B. It uses only one decision tree for prediction.

C. It is a type of neural network.

D. It combines many decision trees to make better predictions.

Solution

1. Step 1: Understand what a random forest is

   A random forest is a group of decision trees working together.

2. Step 2: Identify the main purpose

   Combining many trees helps improve prediction accuracy and reduce errors.

3. Final Answer:

   It combines many decision trees to make better predictions. -> Option D

4. Quick Check:

   Random forest = many trees combined [OK]

Hint: Random forest means many trees working together [OK]

Common Mistakes:

• Thinking it uses only one tree
• Confusing it with neural networks
• Believing it does clustering without labels

2. Which of the following is the correct way to import and create a random forest classifier in Python using scikit-learn?

easy

A. from sklearn.tree import RandomForest
model = RandomForest(100)

B. import randomforest
model = randomforest.RandomForest(100)

C. from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100)

D. from sklearn.ensemble import RandomForest
model = RandomForestClassifier()

Solution

1. Step 1: Check the correct import

   The random forest classifier is in sklearn.ensemble as RandomForestClassifier.

2. Step 2: Verify the model creation syntax

   Use RandomForestClassifier(n_estimators=100) to create with 100 trees.

3. Final Answer:

   from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier(n_estimators=100) -> Option C

4. Quick Check:

   Correct import and model creation [OK]

Hint: Use sklearn.ensemble and RandomForestClassifier [OK]

Common Mistakes:

• Importing from sklearn.tree instead of ensemble
• Using wrong class names
• Missing n_estimators parameter

3. What will be the output of the following code snippet?

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=42)
model = RandomForestClassifier(n_estimators=10, random_state=42)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(list(predictions[:5]))

medium

A. [2, 2, 1, 0, 0]

B. [0, 1, 2, 0, 1]

C. [1, 0, 2, 1, 1]

D. [1, 1, 1, 1, 1]

Solution

1. Step 1: Understand the dataset and split

   The iris dataset has 3 classes (0,1,2). The split is random_state=42 for reproducibility.

2. Step 2: Fit and predict with random forest

   Using 10 trees and random_state=42, predictions on test data are consistent.

3. Final Answer:

   [2, 2, 1, 0, 0] -> Option A

4. Quick Check:

   Predictions match expected output with random_state=42 [OK]

Hint: Set random_state for reproducible predictions [OK]

Common Mistakes:

• Ignoring random_state causing different outputs
• Confusing class labels order
• Assuming all predictions are the same

4. Identify the error in the following code snippet that tries to train a random forest classifier:

from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_trees=100)
model.fit(X_train, y_train)

medium

A. The parameter name should be n_estimators, not n_trees.

B. RandomForestClassifier does not have a fit method.

C. X_train and y_train must be numpy arrays, not lists.

D. The import statement is incorrect.

Solution

1. Step 1: Check parameter names for RandomForestClassifier

   The correct parameter for number of trees is n_estimators, not n_trees.

2. Step 2: Verify other parts of the code

   fit method exists, import is correct, and lists can work if compatible.

3. Final Answer:

   The parameter name should be n_estimators, not n_trees. -> Option A

4. Quick Check:

   Use n_estimators for tree count [OK]

Hint: Use n_estimators to set number of trees [OK]

Common Mistakes:

• Using wrong parameter names
• Thinking fit method is missing
• Assuming data must be numpy arrays

5. You want to improve your random forest classifier's performance on a dataset with many features and some noise. Which combination of hyperparameters is best to try first?

hard

A. Decrease n_estimators and remove max_depth limit to grow full trees.

B. Increase n_estimators and limit max_depth to prevent overfitting.

C. Set max_features to total features and use only 1 tree.

D. Use default parameters without changes.

Solution

1. Step 1: Understand the effect of n_estimators and max_depth

   More trees (higher n_estimators) reduce variance. Limiting max_depth controls overfitting on noisy data.

2. Step 2: Evaluate options for noisy, high-feature data

   Increasing trees and limiting depth balances bias and variance well for such data.

3. Final Answer:

   Increase n_estimators and limit max_depth to prevent overfitting. -> Option B

4. Quick Check:

   More trees + depth limit = better generalization [OK]

Hint: More trees + limit depth to handle noise [OK]

Common Mistakes:

• Using too few trees causing unstable results
• Growing full trees causing overfitting
• Ignoring hyperparameter tuning