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 (A)</p> Signup and view all the answers

What is the formula for Gini Index?

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

What is the formula for Entropy?

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

What is a random forest?

<p>A bag of decision trees using subspace sampling (C)</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 (A)</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 (C)</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 (D)</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 (A)</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. (B)</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. (D)</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. (A)</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. (B)</p> Signup and view all the answers

How are random forests described?

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

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

<p>Pruning (B)</p> Signup and view all the answers

What is the goal of creating ensembles in ensemble learning?

<p>Aggregating the results of different models (B)</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 (A)</p> Signup and view all the answers

What is the goal of pruning in decision trees?

<p>To reduce overfitting by limiting tree depth (A)</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 (C)</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 (D)</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 (C)</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 (D)</p> Signup and view all the answers

In decision trees, what do terminal nodes represent?

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

What is the formula for Gini Index?

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

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

<p>Overfitting (A)</p> Signup and view all the answers

What is minimized to fit a decision tree?

<p>Impurity (A)</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. (B)</p> Signup and view all the answers

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

<p>Pruning techniques (B)</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. (C)</p> Signup and view all the answers

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

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

In decision trees, what do terminal nodes represent?

<p>The final predicted outcome or classification (C)</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 (A)</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 (D)</p> Signup and view all the answers

What is minimized to fit a decision tree?

<p>Variance (B)</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 (D)</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 (C)</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 (D)</p> Signup and view all the answers

How are continuous features handled in decision trees?

<p>By creating binary splits based on threshold values (D)</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 (B)</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. (C)</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. (A)</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. (C)</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. (D)</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. (C)</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. (C)</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. (A)</p> Signup and view all the answers

What does a higher Gini score for a split indicate?

<p>Higher homogeneity in the split. (D)</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. (A)</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. (C)</p> Signup and view all the answers

Flashcards

Decision Tree

A supervised learning method that predicts responses by traversing a tree from root to leaf, using branching rules and trained weights.

Bootstrapping

Sampling with replacement, creating multiple data sets from the original data, where some data points are left out in each new dataset.

Random Forest

A bagged decision tree where each tree is trained on a bootstrap sample and uses random features.

Boosting

Combines weak learners into a strong predictor by iteratively training new trees to minimize errors made by previous models.

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Bagged Decision Trees

Trained independently on bootstrapped data as part of a collection of trees, thus building a prediction forest.

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Classification Tree

Decision tree that predicts class labels for discrete outcomes.

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Regression Tree

Decision tree that predicts continuous values for outcomes.

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CART

Classification and Regression Trees algorithm, creating either classification or regression trees based on the outcome variable.

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Overfitting

A model performing very well on training data but poorly on unseen/testing data, due to memorizing the training data rather than learning the underlying patterns.

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Technical Indicators

Independent variables in financial market analysis, like volatility, momentum, and autocorrelation.

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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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