Decision Tree Algorithms Overview

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

<p>Correctly predicted negative instances out of actual negatives (B)</p> Signup and view all the answers

<p>C4.5 (A)</p> Signup and view all the answers

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

<p>ID3 (B)</p> Signup and view all the answers

<p>It is significantly faster in training time (C)</p> Signup and view all the answers

<p>C4.5 (A)</p> Signup and view all the answers

<p>Conditional Inference Trees (CIT) (C)</p> Signup and view all the answers

<p>To determine the best split at a node (C)</p> Signup and view all the answers

<p>It primarily focuses on continuous variables (C)</p> Signup and view all the answers

The percentage of data points that were correctly categorized by the model. A higher accuracy means the model is more accurate overall.

The percentage of predicted positive instances that were actually correct. For example, if the model predicted 100 cases would be positive, and 80 of them were actually positive, the precision would be 80%.

The percentage of actual positive instances that were correctly identified by the model. If 100 cases were actually positive, and the model identified 75 of them correctly, the recall is 75%.

The percentage of actual negative instances that were correctly identified by the model. If 100 cases were actually negative, and the model correctly identified 90 of them, the specificity would be 90%.

A measure that balances precision and recall, showing how well the model predicts both positive and negative instances. It’s useful when both precision and recall are important.

A widely used decision tree algorithm that can handle both classification and regression tasks. It recursively partitions data into subsets based on feature values, maximizing homogeneity within each subset. A cost function determines the best split at each node, using Gini impurity or entropy to measure node impurity.

A decision tree algorithm that iteratively splits data based on the highest information gain. It primarily focuses on classification tasks using categorical features, but is not suitable for continuous variables.

An improvement upon ID3 designed for both classification and regression. It handles continuous attributes by creating discrete thresholds and uses gain ratio to determine the best split. Gain ratio addresses issues with information gain caused by features with many values. It can handle missing data and creates more concise trees than ID3.

Another successor to C4.5 with increased efficiency for large datasets. It creates faster and more compact trees than its predecessor, making it ideal for large datasets, and utilizes different techniques to optimize performance.

A decision tree algorithm that uses chi-squared tests for splitting, making it suitable for categorical data and useful when a statistically significant relationship between features and the target variable is needed.

This algorithm uses a statistical approach for splitting nodes, primarily for continuous variables. It performs well with large datasets and is known for its faster and simpler split calculation.

A decision tree algorithm that uses statistical inference for tree construction, ensuring reliable, statistically significant splits. It provides a more robust approach to decision tree building.