K-Nearest Neighbors
K-Nearest Neighbors (KNN) is a simple machine learning algorithm that classifies data points based on the majority class of their K closest neighbors. KNN requires no training phase but can be computationally expensive at inference.
Understanding K-Nearest Neighbors
K-nearest neighbors (KNN) is a simple yet effective supervised learning algorithm that classifies new data points based on the majority class among their k closest neighbors in the feature space. As an instance-based or lazy learning method, KNN stores all training examples and performs computation only at inference time, making training trivial but prediction potentially slow on large datasets. The choice of k, the distance metric (commonly Euclidean or Manhattan), and feature scaling all significantly impact performance. KNN is used in recommendation systems, anomaly detection, and pattern recognition, and it serves as an intuitive baseline for classification and regression tasks. Despite its simplicity, KNN can achieve competitive accuracy on many problems, especially when combined with dimensionality reduction techniques to manage the curse of dimensionality that affects high-dimensional 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.