Chapter 6 Part 1

What is the main advantage of using a Random Forest over a single Decision Tree?

• Faster computation

• Reduces overfitting and improves accuracy

• Requires less data

• Needs fewer features

In a Decision Tree, what does each internal node represent?

• Output class

• A test on an attribute/feature

• The average of predictions

• A leaf node

What does “bagging” stand for in ensemble learning?

• Boosting aggregate

• Bootstrap aggregating

• Binary aggregation

• Base grouping

What are the two main techniques used in building a Random Forest?

• K-means and SVM

• Cross-validation and scaling

• Random sampling of data and random feature selection

• Neural nets and normalization

What is the output of a classification Random Forest?

• Probability

• Majority vote of the trees

• Average value

• Single tree prediction

What does “deep” in Deep Learning refer to?

• Large data

• Complex math

• Many layers in the neural network

• Long training time

Which neural network type is best for image recognition?

• RNN

• CNN (Convolutional Neural Network)

• Decision Tree

• SVM

What is the function of the activation function in a neural network?

• Pre-processes the data

• Normalizes output

• Introduces non-linearity to the model

• Selects features

Which of the following is NOT an advantage of Random Forest?

• Handles missing values

• Good for classification

• Easily interpretable

• Works for regression

What does each leaf node in a decision tree represent?

• Root

• Predicted output/class

• Attribute

• Training set