Fundamentals of Machine Learning - Random Forests

The video tutorial introduces random forests, starting with a review of decision trees. It covers loading necessary libraries, explains decision tree basics, and discusses overfitting issues. The tutorial then introduces random forests and bagging, demonstrating how to code them. It concludes with using random forests for regression tasks.

What is the primary purpose of loading libraries like Numpy and Seaborn in the context of decision trees?

• To create interactive web applications

• To enhance the speed of computations

• To visualize data and manage data structures

• To perform complex mathematical operations

How does a decision tree determine where to split the data?

• By using random values

• By averaging all data points

• By clustering data points

• By comparing data points to a threshold

What is a potential downside of a decision tree with too many layers?

• It becomes too simple

• It requires more computational power

• It may overfit the training data

• It cannot handle large datasets

What technique does a random forest use to combine multiple decision trees?

• Pruning

• Bagging

• Stacking

• Boosting

How does the random forest classifier in scikit-learn differ from manually bagging decision trees?

• It uses a different algorithm

• It automatically optimizes parameters

• It is a built-in function that simplifies the process

• It requires more manual coding

What additional functionality does the random forest regressor provide compared to the classifier?

• It requires less data preprocessing

• It predicts continuous values

• It is faster to train

• It can handle categorical data

What is the main advantage of using random forests over a single decision tree?

• They are easier to interpret

• They reduce the risk of overfitting

• They require less data

• They are faster to train