Podcast
Questions and Answers
Which of the following is NOT a characteristic of Decision Trees?
Which of the following is NOT a characteristic of Decision Trees?
What is the purpose of the root node in a Decision Tree?
What is the purpose of the root node in a Decision Tree?
What is the purpose of the leaf nodes in a Decision Tree?
What is the purpose of the leaf nodes in a Decision Tree?
What is the role of entropy or Gini impurity in Decision Tree construction?
What is the role of entropy or Gini impurity in Decision Tree construction?
Signup and view all the answers
Which of the following is a hyperparameter in Decision Tree models?
Which of the following is a hyperparameter in Decision Tree models?
Signup and view all the answers
What is the purpose of visualizing a Decision Tree?
What is the purpose of visualizing a Decision Tree?
Signup and view all the answers
What is the role of decision boundaries in a Decision Tree?
What is the role of decision boundaries in a Decision Tree?
Signup and view all the answers
Which of the following is a common issue that can occur in Decision Trees?
Which of the following is a common issue that can occur in Decision Trees?
Signup and view all the answers
What is the advantage of using Decision Trees over black box models?
What is the advantage of using Decision Trees over black box models?
Signup and view all the answers
Which of the following is a disadvantage of Decision Trees?
Which of the following is a disadvantage of Decision Trees?
Signup and view all the answers
Study Notes
Decision Tree Basics
- A decision tree is composed of nodes, which are chosen to split features optimally.
- The tree stops growing when the maximum depth is reached, which is set by the
max_depthhyperparameter.
Hyperparameter Tuning
-
max_depthis a hyperparameter that controls the depth of the decision tree. - Increasing
max_depthallows the tree to add more decision boundaries and improve its accuracy.
Gini Impurity vs. Entropy
- Gini impurity measures the frequency of mislabels when randomly labeling a dataset.
- Entropy measures the disorder of features with the target.
- Gini impurity is faster and less computationally expensive than entropy.
Gini Impurity
- A node's Gini attribute measures its impurity, with a "pure" node having a Gini score of 0.
- The Gini score can be calculated using the formula: 1 - (p1^2) - (p2^2) - ... - (pk^2), where p1, p2, ..., pk are the proportions of each class in the node.
White Box vs. Black Box Models
- Decision trees are white box models, meaning their decisions are easy to interpret.
- Models like SVM, RF, and neural networks are black box models, meaning their decisions are hard to interpret.
Estimating Class Probabilities
- A decision tree can estimate the probability that an instance belongs to a particular class.
- The estimated probabilities can be identical for some instances.
CART Training Algorithm
- The CART algorithm is used to train decision trees in Scikit-Learn.
- The algorithm recursively splits the training set into subsets using the feature and threshold that produces the purest subsets.
- The cost function that the algorithm tries to minimize is the impurity of the subsets.
- The algorithm stops recursing when it reaches the maximum depth or cannot find a split that reduces impurity.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Description
Test your knowledge on hyperparameter tuning in decision trees, focusing on Gini impurity versus Entropy and the impact of setting the max_depth parameter. Learn about the decision boundaries and node splits in a decision tree.
