Recall & Review
beginner
What is a Random Forest Classifier?
A Random Forest Classifier is a machine learning model that uses many decision trees to make predictions. It combines the results of multiple trees to improve accuracy and reduce mistakes.
Click to reveal answer
intermediate
Why does a Random Forest reduce overfitting compared to a single decision tree?
Because it builds many trees using random samples of data and features, it averages their results. This randomness helps the model avoid fitting too closely to the training data, making it better at predicting new data.
Click to reveal answer
intermediate
What role does 'feature randomness' play in a Random Forest?
Feature randomness means each tree in the forest looks at a random subset of features when splitting nodes. This makes trees different from each other and helps the forest learn more diverse patterns.
Click to reveal answer
beginner
How does a Random Forest make a final prediction for classification?
Each tree in the forest votes for a class label. The Random Forest picks the class that gets the most votes as the final prediction.Click to reveal answer
beginner
What metric can you use to check how well a Random Forest Classifier is performing?
Common metrics include accuracy (how many predictions are correct), precision, recall, and the F1 score. These help understand the model's prediction quality.
Click to reveal answer
What does a Random Forest Classifier use to make predictions?
How does Random Forest reduce overfitting?
What is feature randomness in Random Forest?
How does a Random Forest decide the final class label?
Which metric is NOT typically used to evaluate a Random Forest Classifier?
Explain in your own words how a Random Forest Classifier works and why it is better than a single decision tree.
Describe the role of randomness in building a Random Forest and how it improves model performance.