Decision Trees and Ensemble Learning Quiz
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Questions and Answers

What is the goal of a decision tree?

  • To create complex boundaries for continuous variables
  • To result in a set that minimizes impurity (correct)
  • To segment the predictor space into a large number of complex regions
  • To maximize entropy in the data set

    • How are continuous features handled in decision trees?

  • They are used as root nodes directly
  • They are ignored in the decision-making process
  • They are turned into categorical variables before a split at the root node (correct)
  • They are split into multiple smaller continuous features

    • What is the purpose of pruning in decision trees?

  • To limit tree depth and reduce overfitting (correct)
  • To add more leaf nodes for finer classification
  • To increase the complexity of the tree structure
  • To create deeper decision nodes for better accuracy

    • What does bagging involve in ensemble learning?

    <p>Creating multiple decision trees each trained on a different bootstrap sample of the data</p> Signup and view all the answers

    What is the formula for Gini Index?

    <p>$1 - \sum p_i^2$</p> Signup and view all the answers

    In the Chi-Square algorithm, what does a higher value of Chi-Square indicate?

    <p>Higher statistical significance of differences between sub-node and parent node</p> Signup and view all the answers

    What is the formula for Entropy?

    <p>$-\sum p_i \log_2(p_i)$</p> Signup and view all the answers

    What does the pruning process in decision trees involve?

    <p>Starting at the bottom and removing leaves with negative returns compared to the top</p> Signup and view all the answers

    What is the purpose of maximum features to consider for split in decision trees?

    <p>To limit the number of features considered while searching for a best split</p> Signup and view all the answers

    What does a higher value of Information Gain indicate?

    <p>A less impure node requires less information to describe it</p> Signup and view all the answers

    What does a higher Gini score for a split indicate?

    <p>Higher homogeneity within each subset after splitting</p> Signup and view all the answers

    In variance reduction, what does calculating variance for each node help determine?

    <p>The spread or dispersion of data within each node</p> Signup and view all the answers

    What does setting constraints on tree size, such as maximum depth, help prevent?

    <p>Overfitting by creating an overly complex model</p> Signup and view all the answers

    In decision trees, what do terminal nodes represent?

    <p>Final prediction outcomes or subsets at the end of branches</p> Signup and view all the answers

    What is bootstrapping in the context of decision trees?

    <p>Sampling with replacement, where some data is left out of each tree in the sample</p> Signup and view all the answers

    What is a random forest?

    <p>A bag of decision trees using subspace sampling</p> Signup and view all the answers

    How does boosting work in supervised learning?

    <p>Aggregates weak learners to form a strong predictor by adding new trees that minimize the error of previous learners</p> Signup and view all the answers

    How do decision trees predict responses?

    <p>By following decisions in the tree from the root to a leaf node, using branching conditions and trained weights</p> Signup and view all the answers

    What distinguishes bagged decision trees from boosting?

    <p>Bagged decision trees consist of independently trained trees on bootstrapped data, while boosting adds weak learners iteratively</p> Signup and view all the answers

    What are CART's outputs based on the nature of the dependent variable?

    <p>Classification or regression trees</p> Signup and view all the answers

    How do regression trees differ from classification trees?

    <p>Regression trees predict continuous values, while classification trees represent class labels on leaves and conjunctions of features on branches.</p> Signup and view all the answers

    What is minimized to fit a decision tree?

    <p>A loss function, choosing the best variable and splitting value among all possibilities.</p> Signup and view all the answers

    What are used to ensure interpretability and prevent overfitting in decision trees?

    <p>Criteria like maximum depth, node size, and pruning.</p> Signup and view all the answers

    In what context are technical indicators like volatility and momentum used as independent variables?

    <p>In a financial market context.</p> Signup and view all the answers

    How are random forests described?

    <p>Ensembles of random trees, like bootstrapping with decision trees</p> Signup and view all the answers

    What is the computational measure of the impurity of elements in a set used in decision trees?

    <p>Shannon's Entropy Model</p> Signup and view all the answers

    What is the method of limiting tree depth to reduce overfitting in decision trees?

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

    What is the goal of creating ensembles in ensemble learning?

    <p>Aggregating the results of different models</p> Signup and view all the answers

    What does bagging involve in ensemble learning?

    <p>Creating multiple decision trees each trained on a different bootstrap sample of the data</p> Signup and view all the answers

    What is the goal of pruning in decision trees?

    <p>To reduce overfitting by limiting tree depth</p> Signup and view all the answers

    How are continuous features handled in decision trees?

    <p>They are turned into categorical variables before a split at the root node</p> Signup and view all the answers

    What distinguishes bagged decision trees from boosting?

    <p>Bagging involves creating multiple decision trees trained on different bootstrap samples, while boosting adapts the weights of data points at each iteration</p> Signup and view all the answers

    What is minimized to fit a decision tree?

    <p>Impurity to result in a set that minimizes impurity</p> Signup and view all the answers

    In the context of decision trees, what does a higher Gini score for a split indicate?

    <p>Higher impurity within the split</p> Signup and view all the answers

    In decision trees, what do terminal nodes represent?

    <p>Predicted outcomes</p> Signup and view all the answers

    What is the formula for Gini Index?

    <p>$G = 1 - \sum_{i=1}^{n} p_i^2$</p> Signup and view all the answers

    In decision trees, what is the purpose of maximum features to consider for split?

    <p>To limit the number of features considered for each split</p> Signup and view all the answers

    What does a higher value of Information Gain indicate?

    <p>Less impure node requires less information to describe it</p> Signup and view all the answers

    In the Chi-Square algorithm, what does a higher value of Chi-Square indicate?

    <p>Higher statistical significance between sub-nodes and parent node</p> Signup and view all the answers

    What does setting constraints on tree size, such as maximum depth, help prevent?

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

    What is minimized to fit a decision tree?

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

    What distinguishes bagged decision trees from boosting?

    <p>Bagging aims to reduce variance while boosting aims to reduce bias.</p> Signup and view all the answers

    What are used to ensure interpretability and prevent overfitting in decision trees?

    <p>Pruning techniques</p> Signup and view all the answers

    How does boosting work in supervised learning?

    <p>It sequentially trains multiple models to correct errors made by previous models.</p> Signup and view all the answers

    What is the formula for Gini Index used in decision trees?

    <p>$Gini = 1 - igg(\sum_{i=1}^{n} P_i^2\bigg)$</p> Signup and view all the answers

    What is the purpose of pruning in decision trees?

    <p>To prevent overfitting by reducing the size of the tree</p> Signup and view all the answers

    In decision trees, what do terminal nodes represent?

    <p>The final predicted outcome or classification</p> Signup and view all the answers

    What does a higher value of Chi-Square indicate in the Chi-Square algorithm?

    <p>Higher statistical significance of differences between sub-node and parent node</p> Signup and view all the answers

    What distinguishes bagged decision trees from boosting in ensemble learning?

    <p>Bagging builds multiple models independently, while boosting builds models sequentially to correct errors</p> Signup and view all the answers

    What is minimized to fit a decision tree?

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

    What is the computational measure of the impurity of elements in a set used in decision trees?

    <p>Gini Index</p> Signup and view all the answers

    What is the goal of creating ensembles in ensemble learning?

    <p>To reduce both bias and variance simultaneously</p> Signup and view all the answers

    What does a higher value of Information Gain indicate?

    <p>Less impure node requiring less information to describe it</p> Signup and view all the answers

    How are continuous features handled in decision trees?

    <p>By creating binary splits based on threshold values</p> Signup and view all the answers

    What distinguishes regression trees from classification trees?

    <p>Regression trees predict continuous outcomes, while classification trees predict categorical outcomes</p> Signup and view all the answers

    What distinguishes bagged decision trees from boosting?

    <p>Bagged decision trees consist of independently trained trees on bootstrapped data, while boosting adds weak learners iteratively.</p> Signup and view all the answers

    What is the purpose of maximum depth, node size, and pruning in decision trees?

    <p>To ensure interpretability and prevent overfitting in decision trees.</p> Signup and view all the answers

    How are technical indicators like volatility and momentum used as independent variables?

    <p>As independent variables in a financial market context.</p> Signup and view all the answers

    What is minimized to fit a decision tree?

    <p>A loss function is minimized to fit a decision tree, choosing the best variable and splitting value among all possibilities.</p> Signup and view all the answers

    What does setting constraints on tree size, such as maximum depth, help prevent?

    <p>Overfitting in decision trees.</p> Signup and view all the answers

    What is bootstrapping in the context of decision trees?

    <p>Bootstrapping involves sampling with replacement, where some data is left out of each tree in the sample.</p> Signup and view all the answers

    How do regression trees differ from classification trees?

    <p>Regression trees predict continuous values, while classification trees represent class labels on leaves and conjunctions of features on branches.</p> Signup and view all the answers

    What does a higher Gini score for a split indicate?

    <p>Higher homogeneity in the split.</p> Signup and view all the answers

    What are CART's outputs based on the nature of the dependent variable?

    <p>CART produces classification or regression trees, depending on the dependent variable's nature.</p> Signup and view all the answers

    What is a random forest?

    <p>Random forests are ensembles of random trees, like bootstrapping with decision trees using randomly selected features.</p> Signup and view all the answers

    Study Notes

    Decision Trees, Bagged and Boosted Decision Trees in Supervised Learning

    • Bootstrapping involves sampling with replacement, where some data is left out of each tree in the sample.
    • A bag of decision trees using subspace sampling is known as a random forest.
    • Boosting aggregates weak learners to form a strong predictor by adding new trees that minimize the error of previous learners.
    • Decision trees predict responses by following decisions in the tree from the root to a leaf node, using branching conditions and trained weights.
    • Bagged decision trees consist of independently trained trees on bootstrapped data, while boosting adds weak learners iteratively.
    • Decision trees are formed by rules based on variables in the data set, with splitting stopping when no further gain can be made or pre-set stopping rules are met.
    • CART produces classification or regression trees, depending on the dependent variable's nature.
    • Classification trees represent class labels on leaves and conjunctions of features on branches, while regression trees predict continuous values.
    • A loss function is minimized to fit a decision tree, choosing the best variable and splitting value among all possibilities.
    • Criteria like maximum depth, node size, and pruning are used to ensure interpretability and prevent overfitting in decision trees.
    • Technical indicators like volatility, short-term and long-term momentum, short-term reversal, and autocorrelation regime are used as independent variables in a financial market context.
    • Random forests are ensembles of random trees, like bootstrapping with decision trees using randomly selected features.

    Decision Trees, Bagged and Boosted Decision Trees in Supervised Learning

    • Bootstrapping involves sampling with replacement, where some data is left out of each tree in the sample.
    • A bag of decision trees using subspace sampling is known as a random forest.
    • Boosting aggregates weak learners to form a strong predictor by adding new trees that minimize the error of previous learners.
    • Decision trees predict responses by following decisions in the tree from the root to a leaf node, using branching conditions and trained weights.
    • Bagged decision trees consist of independently trained trees on bootstrapped data, while boosting adds weak learners iteratively.
    • Decision trees are formed by rules based on variables in the data set, with splitting stopping when no further gain can be made or pre-set stopping rules are met.
    • CART produces classification or regression trees, depending on the dependent variable's nature.
    • Classification trees represent class labels on leaves and conjunctions of features on branches, while regression trees predict continuous values.
    • A loss function is minimized to fit a decision tree, choosing the best variable and splitting value among all possibilities.
    • Criteria like maximum depth, node size, and pruning are used to ensure interpretability and prevent overfitting in decision trees.
    • Technical indicators like volatility, short-term and long-term momentum, short-term reversal, and autocorrelation regime are used as independent variables in a financial market context.
    • Random forests are ensembles of random trees, like bootstrapping with decision trees using randomly selected features.

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    lecture 06.pdf

    Description

    Test your knowledge on decision trees, pruning, ensemble learning, bagging, random forest, and boosting with this quiz. Learn about the flowchart-like structure of decision trees and how they are used in supervised learning algorithms.

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