What is a key feature of Random Forests that helps in feature selection?
Practice Questions
1 question
Q1
What is a key feature of Random Forests that helps in feature selection?
It uses all features for every tree
It randomly selects a subset of features for each split
It eliminates all features with low variance
It requires manual feature selection
Random Forests randomly select a subset of features for each split, which helps in feature selection and reduces overfitting.
Questions & Step-by-step Solutions
1 item
Q
Q: What is a key feature of Random Forests that helps in feature selection?
Solution: Random Forests randomly select a subset of features for each split, which helps in feature selection and reduces overfitting.
Steps: 6
Step 1: Understand that Random Forests are a type of machine learning model used for classification and regression.
Step 2: Know that Random Forests create many decision trees to make predictions.
Step 3: Realize that for each decision tree, Random Forests randomly choose a small number of features from the total available features.
Step 4: This random selection of features means that not all features are used in every tree, which helps to focus on the most important ones.
Step 5: By using only a subset of features for each split in the trees, Random Forests can reduce the chance of overfitting, which is when a model learns too much from the training data and performs poorly on new data.
Step 6: As a result, this process helps in identifying which features are more important for making predictions.
