Decision Trees and Ensemble Learning Quiz

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Questions and Answers

What is the goal of a decision tree?

To maximize entropy in the resulting set

To create infinite boundaries for continuous variables

To minimize impurity in the resulting set (correct)

To overfit the model

What does pruning in decision trees aim to achieve?

Increase overfitting by expanding tree depth

Minimize information gain by removing nodes

Maximize impurity by adding more nodes

Reduce overfitting by limiting tree depth (correct)

What is the purpose of creating ensembles in decision trees?

Maximizing overfitting by combining similar models

Aggregating the results of different models (correct)

Selecting the best single model for prediction

Minimizing diversity among different models

How are continuous features handled in decision tree splits?

Turned into categorical variables before split at the root node Signup and view all the answers

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

$(p^2+q^2)$ Signup and view all the answers

What does a higher Gini Index value indicate in decision tree splits?

Higher impurity Signup and view all the answers

What is the goal of using Chi-Square in decision tree splits?

To find statistical significance between sub-nodes and parent node Signup and view all the answers

What is the formula for Chi-Square used in decision trees?

$((Actual – Expected)^2 / Expected)^{1/2}$ Signup and view all the answers

What does a higher Chi-Square value indicate in decision tree splits?

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

What is the goal of using Information Gain in decision tree splits?

To calculate information gain for each node Signup and view all the answers

What is the name for a bag of decision trees using subspace sampling?

Random forest Signup and view all the answers

How does boosting form a strong predictor?

By adding new trees that minimize the error of previous learners Signup and view all the answers

What do decision trees predict responses by following?

Decisions in the tree from the root to a leaf node Signup and view all the answers

What distinguishes bagged decision trees from boosting?

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

What determines whether splitting stops in decision trees?

When no further gain can be made or pre-set stopping rules are met Signup and view all the answers

What does CART produce?

Classification or regression trees, depending on the dependent variable's nature Signup and view all the answers

What do classification trees represent on leaves and branches?

Class labels on leaves and conjunctions of features on branches Signup and view all the answers

What do regression trees predict?

Continuous values Signup and view all the answers

What is minimized to fit a decision tree?

A loss function, choosing the best variable and splitting value among all possibilities Signup and view all the answers

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

Maximum depth, node size, and pruning Signup and view all the answers

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

Financial market context Signup and view all the answers

What are random forests?

Ensembles of random trees, like bootstrapping with decision trees using randomly selected features Signup and view all the answers

What is the goal of pruning in decision trees?

To reduce overfitting by limiting tree depth Signup and view all the answers

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

Shannon’s Entropy Model Signup and view all the answers

What does Bagging involve in ensemble learning for decision trees?

Creating multiple decision trees, each trained on a different bootstrap sample of the data Signup and view all the answers

What is the primary factor used to make the decision on which feature to split on in decision trees?

Resultant entropy reduction or information gain from the split Signup and view all the answers

What does ensemble learning aim to achieve in decision trees?

Aggregating the results of different models to improve accuracy and robustness Signup and view all the answers

What is the main difference between random forests and boosting?

Random forests use subspace sampling while boosting aggregates weak learners. Signup and view all the answers

How are bagged decision trees and boosting similar in creating ensembles?

Both combine weaker trees into stronger ensembles, with bagging using independent trees and boosting iteratively adding weak learners. Signup and view all the answers

What is the goal of using technical indicators like volatility and momentum in market analysis?

To predict market behavior and probabilities of returns by using their combinations. Signup and view all the answers

What does CART (Classification and Regression Trees) produce?

Non-parametric techniques producing either classification or regression trees based on the dependent variable. Signup and view all the answers

How are decision trees formed?

By recursively splitting nodes based on variables' values until no further gain or stopping rules are met. Signup and view all the answers

What is the formula for weighted Gini for split by PB?

$(10/30)*0.68+(20/30)*0.55$ Signup and view all the answers

What does the Chi-Square value indicate in decision tree splits?

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

What does a lower entropy value for a node indicate?

Less impure node requiring less information to describe it Signup and view all the answers

What is the purpose of pruning in decision trees?

To prevent overfitting and improve interpretability by removing unnecessary leaves Signup and view all the answers

What is the goal of creating ensembles in decision trees?

To improve predictive performance and reduce overfitting 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, and Technical Indicators in Market Analysis

Bootstrapping involves sampling with replacement, leaving some data out in each tree, and is used to create a random forest with subspace sampling.
Random forests are a collection of decision trees using subspace sampling, while boosting aggregates weak learners to form a strong predictor over time.
A boosted model adds new trees to minimize errors by previous learners, fitting new trees on residuals of previous trees.
Decision trees predict data responses by following branching conditions and trained weights, and can be pruned for model simplification.
Bagged decision trees and boosting combine weaker trees into stronger ensembles, with bagging using independent trees and boosting iteratively adding weak learners.
Decision trees are formed by rules based on variables' values, recursively splitting nodes until no further gain or stopping rules are met.
Classification and regression trees (CART) are non-parametric techniques producing either classification or regression trees based on the dependent variable.
Classification trees represent class labels in leaves and conjunctions of features in branches, while regression trees predict continuous values.
Decision trees are built by minimizing a loss function, considering the best variable and splitting value and using criteria to ensure interpretability and prevent overfitting.
Technical indicators in market analysis include volatility, short-term momentum, long-term momentum, short-term reversal, and autocorrelation regime.
Each technical indicator has binary outcomes, and their combinations can be used to predict market behavior and probabilities of returns.
Random forests are created by bootstrapping with decision trees and randomly selecting features, while bootstrapping involves sampling with replacement to create subsets of data.

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Description

Test your knowledge of 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 to segment predictor spaces into simple regions based on significant features.