In Random Forests, how are the individual trees trained?
Practice Questions
Q1
In Random Forests, how are the individual trees trained?
On the entire dataset without any modifications.
Using a bootstrapped sample of the dataset.
On a subset of features only.
Using the same random seed for all trees.
Questions & Step-by-Step Solutions
In Random Forests, how are the individual trees trained?
Step 1: Start with your original dataset, which contains all the data points.
Step 2: Create a bootstrapped sample by randomly selecting data points from the original dataset. This means you can pick the same data point more than once, and some data points may not be selected at all.
Step 3: Use this bootstrapped sample to train the first decision tree. This tree will learn patterns based on the data points in this sample.
Step 4: Repeat Steps 2 and 3 multiple times to create many different bootstrapped samples and train a separate decision tree for each sample.
Step 5: Each tree will be slightly different because they are trained on different samples, which helps to create diversity among the trees in the Random Forest.
