Lazy Learning
Lazy learning is an approach where the model delays computation until a query is made rather than building a model during training. K-Nearest Neighbors is the most well-known lazy learning algorithm.
Understanding Lazy Learning
Lazy learning is a machine learning approach where the model defers all computation until a prediction is actually requested, rather than building an explicit model during a separate training phase. The k-nearest neighbors algorithm is the most well-known example, storing all training data and computing distances to find similar instances only at query time. This contrasts with eager learning methods like decision trees or neural networks that invest significant effort upfront to construct a generalized model. Lazy learning has several advantages: it naturally handles multi-modal distributions, adapts instantly to new training data, and makes no assumptions about the underlying data distribution. However, it can be computationally expensive at inference time and requires efficient data structures for fast neighbor lookups. Lazy learning remains relevant in recommendation systems, anomaly detection, and imputation tasks where local data patterns are more informative than global models.
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.