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Decision Tree Hyperparameter Tuning Quiz
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Decision Tree Hyperparameter Tuning Quiz

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Questions and Answers

Which of the following is NOT a characteristic of Decision Trees?

  • They are white box models
  • They have a hierarchical tree structure
  • They can be used for classification and regression tasks
  • They are black box models (correct)
  • What is the purpose of the root node in a Decision Tree?

  • To split the data based on the most important feature (correct)
  • To combine the predictions from different branches
  • To represent the final prediction
  • To calculate the entropy or Gini impurity
  • What is the purpose of the leaf nodes in a Decision Tree?

  • To calculate the entropy or Gini impurity
  • To represent the final prediction (correct)
  • To split the data based on the least important feature
  • To combine the predictions from different branches
  • What is the role of entropy or Gini impurity in Decision Tree construction?

    <p>To measure the impurity or uncertainty of a node</p> Signup and view all the answers

    Which of the following is a hyperparameter in Decision Tree models?

    <p>The maximum depth of the tree</p> Signup and view all the answers

    What is the purpose of visualizing a Decision Tree?

    <p>To interpret the decision-making process</p> Signup and view all the answers

    What is the role of decision boundaries in a Decision Tree?

    <p>To separate the data into distinct regions for different classes</p> Signup and view all the answers

    Which of the following is a common issue that can occur in Decision Trees?

    <p>Overfitting</p> Signup and view all the answers

    What is the advantage of using Decision Trees over black box models?

    <p>Interpretability and transparency of the decision-making process</p> Signup and view all the answers

    Which of the following is a disadvantage of Decision Trees?

    <p>Instability to small changes in the data</p> 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_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.

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