Random forest in depth in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a random forest in machine learning?

A random forest is a group of decision trees working together. Each tree makes a prediction, and the forest picks the most common answer. This helps make better and more stable predictions.

beginner

Why does random forest use many decision trees instead of one?

Using many trees reduces mistakes from any single tree. It lowers errors by averaging many opinions, making the final prediction more accurate and less likely to be wrong.

intermediate

What is 'bagging' in the context of random forests?

Bagging means making many trees from different random samples of the data. Each tree sees a slightly different set of data, which helps the forest learn better and avoid overfitting.

intermediate

How does random forest select features when splitting nodes?

At each split, random forest picks a random small group of features and chooses the best split only from them. This randomness helps trees be different and improves the forest's overall strength.

beginner

What metrics can we use to check a random forest's performance?

We can use accuracy, precision, recall, F1 score for classification tasks, and mean squared error or R-squared for regression tasks. These metrics tell us how well the forest predicts.

What does each tree in a random forest use to make splits?

A A random subset of features

B All features every time

C Only the most important feature

D Features selected by the user

Random forest selects a random subset of features at each split to create diverse trees.

What is the main benefit of using many trees in a random forest?

A To use more memory

B To make the model slower

C To confuse the user

D To reduce overfitting and improve accuracy

Many trees help reduce overfitting and improve the model's accuracy by averaging predictions.

What is 'bagging' short for in random forests?

A Basic aggregation

B Bagging groceries

C Bootstrap aggregating

D Binary aggregation

Bagging stands for bootstrap aggregating, which means training trees on random samples with replacement.

Which metric is NOT typically used to evaluate a random forest classifier?

A Mean squared error

B Accuracy

C Precision

D Recall

Mean squared error is used for regression, not classification.

How does random forest help prevent overfitting?

A By using only one tree

B By averaging many trees built on random data and features

C By ignoring data points

D By using all features at every split

Random forest averages many trees trained on random samples and features, reducing overfitting.

Explain how random forest builds its model and why it is more reliable than a single decision tree.

Think about how many opinions together make a better decision.

Describe the role of randomness in random forest and how it improves model performance.

Randomness helps trees learn different things.