Decision Trees in Machine Learning

A decision tree is a flowchart-like structure that visualizes a decision-making process by branching out based on various conditions (tests) to arrive at a final outcome (prediction).

Each node in a decision tree corresponds to a test (question) that divides the data into different branches based on the answer. The tests are usually based on input attributes (features) of the data.

Leaf nodes represent the final decisions or predictions made based on the sequence of tests taken.

In a decision tree, each path from the root node to a leaf node represents a unique combination of test results, leading to a specific prediction.

The decision tree represents a function that maps input data to a specific output.

Boolean functions, expressed using true/false values, can be represented using decision trees.

Decision trees can handle continuous attributes by splitting the data based on thresholds or ranges instead of individual values.

Decision trees are used in various machine learning tasks, such as classification and regression. They can be used to predict categorical outcomes or estimate numerical values.

A tree-like structure used in machine learning to make decisions by branching out based on tests performed on input data.

In a decision tree, each node represents a test performed on the input data, splitting the data into different branches based on the result of the test.

Leaf nodes in a decision tree represent the final prediction or classification made for a particular data instance, based on the sequence of tests taken from the root node to the leaf.

A decision tree where the target variable (Y) is a category, like 'yes' or 'no'.

The process of choosing the best test to perform at each node in a decision tree, aiming to maximize the information gained about the target variable.

A measure used in information theory to quantify the uncertainty or information content of a random variable. In decision trees, entropy helps determine the best split based on information gain.

A decision tree where the target variable (Y) is a number, like temperature or price.

The gain in information achieved by performing a particular test in a decision tree node, measured by the reduction in entropy resulting from the split.

The process of finding a decision tree that best fits the data, using a specific algorithm.

Information gain is a criterion used to select the best test at each node in a decision tree. It prioritizes tests that result in the largest reduction in entropy, maximizing information about the target variable.

A decision tree that perfectly predicts all the data points in the training set.

A decision tree that makes accurate predictions on new data, not just the training set.

Decision trees can handle continuous attributes by splitting the data based on thresholds or ranges of values, instead of individual values as in discrete attributes.

The expected value of how badly a decision tree performs on new data, calculated using a loss function.

Finding a decision tree that is small and accurate, using heuristics and only the training data.

The process of building a decision tree by starting with the full data set and repeatedly splitting it based on the best feature to predict the target variable.

The process of finding the best feature to split the data in a decision tree.

A subset of data that has a relatively homogeneous value for the target variable.

A measure of how uncertain we are about the class of an instance in a dataset.

The process of choosing the best attribute or question to split the data at each node in a decision tree. The question that maximizes information gain is usually chosen.

The ability of a decision tree to represent both continuous (e.g., temperature) and discrete (e.g., color) attributes.

The process of evaluating a decision tree's performance. Metrics like accuracy or error rate are used.

The process of simplifying a complex decision tree by removing redundant or unnecessary branches. It can improve the tree's readability and efficiency.

The process of predicting a numerical value based on input features using a decision tree.