Linear Regression
Linear regression is a statistical method that models the relationship between a dependent variable and one or more independent variables using a linear equation. It is one of the simplest and most interpretable ML algorithms.
Understanding Linear Regression
Linear regression is one of the most fundamental machine learning algorithms, modeling the relationship between input features and a continuous output as a weighted linear combination. The model learns coefficients (weights) that minimize the sum of squared differences between predicted and actual values, providing both predictions and interpretable insights into feature importance. Despite its simplicity, linear regression is widely used in economics for forecasting, in real estate for price estimation, and in science for analyzing experimental relationships. Extensions like ridge and lasso regression add regularization to prevent overfitting and perform feature selection. Linear regression serves as an important baseline against which more complex models are compared, and understanding it is essential for grasping advanced techniques like logistic regression and neural networks, which build on similar mathematical foundations involving the loss function and gradient descent.
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.