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Questions and Answers
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 leaf nodes in a Decision Tree?
What is the role of entropy or Gini impurity in Decision Tree construction?
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Which of the following is a hyperparameter in Decision Tree models?
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What is the purpose of visualizing a Decision Tree?
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What is the role of decision boundaries in a Decision Tree?
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Which of the following is a common issue that can occur in Decision Trees?
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What is the advantage of using Decision Trees over black box models?
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Which of the following is a disadvantage of Decision Trees?
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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_depth
hyperparameter.
Hyperparameter Tuning
-
max_depth
is a hyperparameter that controls the depth of the decision tree. - Increasing
max_depth
allows 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.
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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.