Practical Data Science using Python - Random Forest Steps Pruning and Optimization

The video tutorial explains decision trees and random forests, focusing on their structure, hyperparameters, and the bagging process. It highlights the importance of hyperparameters like Gini index and entropy in optimizing models and preventing overfitting. The tutorial also covers the out of bag score for model validation and the steps to build and use random forests. Additionally, it discusses feature importance and its role in identifying influential features. The tutorial concludes with a practical application of random forests in predicting loan defaults using historical financial data.

What is a key difference between a decision tree and a random forest?

• A random forest is a single tree, while a decision tree is an ensemble.

• A decision tree uses bagging, while a random forest does not.

• A decision tree uses a single dataset, while a random forest uses subsets of data.

• A random forest uses a single feature, while a decision tree uses multiple features.

Which hyperparameter is used to control the depth of a decision tree?

• Max depth

• Min samples split

• Min samples leaf

• Max features

What is the purpose of the 'n_jobs' parameter in random forests?

• To choose the number of features for each split

• To set the number of CPU cores used for computation

• To determine the maximum depth of each tree

• To specify the number of trees in the forest

Which method is used to find the optimal hyperparameters for a random forest?

• Grid search with cross-validation

• Random sampling

• Manual tuning

• Bootstrap aggregation

What is the default value of 'n_estimators' in a random forest?

• 200

• 100

• 10

• 500

How does the out-of-bag (OOB) score benefit the evaluation of a random forest model?

• It increases the number of trees in the forest.

• It decreases the computational cost of training.

• It allows evaluation without a separate validation set.

• It requires a separate validation set for evaluation.

What is the role of the 'max_samples' parameter in random forests?

• To set the maximum number of features

• To determine the maximum depth of trees

• To specify the percentage of data used for each subsample

• To choose the number of trees in the forest

What is the first step in building a random forest model?

• Calculating the OOB score

• Selecting random samples of observations

• Choosing random subsets of features

• Determining the number of trees

What does the 'feature_importances_' attribute in a random forest model indicate?

• The accuracy of the model

• The number of trees in the forest

• The importance of each feature in the model

• The depth of each tree

How does a random forest model make a final prediction?

• By voting on the predictions of all trees

• By averaging the predictions of all trees

• By selecting the prediction of the first tree

• By using the prediction of the tree with the highest accuracy