Decision Trees in Machine Learning

A decision tree is a machine learning model that predicts outcomes based on a series of decisions. The model is structured like a tree with branching nodes, each representing a decision to be made based on the value of a feature.

Each internal node in a decision tree represents a feature or attribute. It's like a question that splits the data into different branches based on the answer.

Leaf nodes are the end points of the decision tree. They represent the predicted outcome for a given path through the tree.

The process of creating a decision tree involves splitting the data into subsets based on the values of features. The goal is to create a tree that accurately predicts outcomes while minimizing complexity.

A feature used to split the data at an internal node of a decision tree. It helps make a decision on which branch to take.

A method for creating a predictive model by repeatedly dividing data into smaller subsets based on attribute values. It aims to develop a decision tree that can classify or predict outcomes, ultimately leading to a tree that covers all training data.

A class of problems well-suited to Decision Tree Learning, characterized by data points described by attribute values and target functions that have distinct categories or classifications. These problems also benefit from having the ability to express the target function as a combination of logical conditions, making them suitable for decision tree representations.

A data structure that resembles a tree, where nodes represent attribute values and branches represent decisions based on those values. It can be used to classify or predict outcomes based on input attribute values.

A logical expression that can capture the decision rules represented by a decision tree.

Amount of information, measured in bits. Higher entropy means more uncertainty.

Measures how much a feature improves prediction accuracy. Higher gain means better splitting ability.

The set of training examples used to build a decision tree.

The collection of all possible outcomes or predictions for a decision tree.

The process of using a decision tree to predict an outcome for a new instance.

A measure of how well a decision tree performs in predicting outcomes.

The number of training examples that belong to a specific outcome.

A subset of training examples with the same value for a specific feature.

The ratio of 'yes' outcomes to the total number of outcomes in a subset.

The process of simplifying a decision tree by merging or removing nodes to reduce complexity.

The number of 'yes' outcomes and 'no' outcomes in a subset.

A decision tree learning technique where attributes are evaluated based on their information gain. The attribute with the highest gain is chosen for splitting the data.

The set of all possible values for an attribute, used in decision tree learning.

The process of dividing data into subsets based on the values of an attribute.

A measure of the uncertainty in a subset of the data after splitting based on an attribute.

A representation of the data split based on its values, used to calculate information gain.

The attribute with the highest information gain is selected to split the data first, and then further splits are made based on other attributes.

The data used in decision tree learning to train the model. It includes examples of different attributes and the corresponding outcome.

The process of determining which attribute to use for splitting based on the highest information gain.

A statistical criterion used to identify the best attribute for splitting the data during decision tree learning.

An example of a decision tree learning algorithm that uses information gain to split data into different decision paths.

The outcome or classification that decision-tree models aim to predict based on input attributes.

The process of organizing data in a decision tree by splitting it into subsets based on the value of features.

The ability of a decision tree to make accurate predictions based on new, unseen data.

A technique for constructing a decision tree where the splitting feature at each internal node is chosen based on its ability to maximize Information Gain.

A way to measure the complexity of a decision tree, showing its interconnectedness and number of levels.

Overfitting occurs when a machine learning model learns the training data so well that it fails to generalize to new, unseen data, resulting in poor performance on real-world data.

The process of identifying and reducing overfitting in a machine learning model. This can involve techniques like pruning, regularization, or using a validation set.