Test Your Knowledge
Question 1 of 4
How do decision trees make predictions?
By clustering similar data points together
By calculating the distance between data points
By sequentially splitting data based on feature questions until reaching a final prediction
By finding a linear relationship between variables
Question 2 of 4
What is information gain used for in decision trees?
To determine the final prediction at leaf nodes
To evaluate the model's overall performance
To choose the best features and splits at each node
To calculate the computational cost of the algorithm
Question 3 of 4
What is entropy in the context of decision trees?
The speed at which the tree makes predictions
The maximum depth of the tree
A measure of impurity or disorder in the data at a node
The number of features used in the tree
Question 4 of 4
What is one key advantage of decision trees compared to other machine learning algorithms?
They always provide the highest accuracy
They are extremely fast to train on large datasets
They are easy to interpret and explain
They never overfit the training data