Regression
Regression is a supervised learning task where the model predicts a continuous numerical value rather than a category. Examples include predicting house prices, stock returns, and temperature forecasts.
Understanding Regression
Regression is a fundamental supervised learning task that predicts continuous numerical values based on input features, as opposed to classification, which predicts discrete categories. Linear regression, the simplest form, models the relationship between variables as a straight line, while polynomial regression captures nonlinear patterns, and neural network-based regression can learn arbitrarily complex mappings. Real-world regression applications are ubiquitous: predicting housing prices based on property features, forecasting sales revenue, estimating patient recovery times, and modeling climate variables. Evaluation metrics for regression include mean squared error, mean absolute error, and R-squared, each capturing different aspects of prediction quality. Regularization techniques like L1 (Lasso) and L2 (Ridge) regression add penalty terms to prevent overfitting by constraining the magnitude of model parameters. Regression analysis also provides interpretability through learned coefficients that quantify the relationship between each input feature and the predicted outcome.
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.