Information Gain
Information gain measures the reduction in entropy achieved by splitting data on a particular feature. It is the primary criterion for building decision trees and feature selection.
Understanding Information Gain
Information gain measures how much a particular feature or attribute reduces uncertainty about the target variable, making it a fundamental concept in decision tree learning and feature selection. Mathematically, it is based on the reduction in entropy—or alternatively, the Kullback-Leibler divergence—between the original distribution and the conditional distribution after splitting on a feature. The ID3 and C4.5 decision tree algorithms use information gain as their primary splitting criterion to build interpretable classification models. Beyond decision trees, information gain is valuable for understanding which features contribute most to model predictions, supporting explainability in machine learning. It is commonly used in text classification for selecting the most discriminative words, in extractive summarization for identifying important sentences, and in benchmark design for evaluating how well models capture meaningful patterns versus memorizing noise in the training data.
Category
Machine Learning
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Accuracy
Accuracy is a metric that measures the proportion of correct predictions out of total predictions made by a model. While intuitive, accuracy can be misleading on imbalanced datasets where one class dominates.
Active Learning
Active learning is a machine learning approach where the model selectively queries an oracle (often a human) for labels on the most informative data points. This reduces the total amount of labeled data needed to train an accurate model.
Anomaly Detection
Anomaly detection is the identification of data points, events, or patterns that deviate significantly from expected behavior. AI-based anomaly detection is used in fraud prevention, cybersecurity, and industrial monitoring.
AutoML
Automated Machine Learning (AutoML) is the process of automating the end-to-end pipeline of applying machine learning, including feature engineering, model selection, and hyperparameter tuning. AutoML democratizes AI by reducing the expertise required.
Bagging
Bagging (Bootstrap Aggregating) is an ensemble technique that trains multiple models on random subsets of training data and combines their predictions. Random Forest is the most well-known bagging-based algorithm.
Bayesian Network
A Bayesian network is a probabilistic graphical model that represents variables and their conditional dependencies using a directed acyclic graph. It enables reasoning under uncertainty and causal inference.
Bias-Variance Tradeoff
The bias-variance tradeoff is the fundamental tension in machine learning between model simplicity (high bias) and model flexibility (high variance). Optimal models balance underfitting and overfitting to generalize well to new data.
Binary Classification
Binary classification is a supervised learning task where the model assigns inputs to one of exactly two categories. Spam detection (spam vs. not spam) and medical diagnosis (positive vs. negative) are common examples.