Online Learning
Online learning is a training paradigm where the model updates its parameters incrementally as new data arrives, rather than retraining on the entire dataset. It is essential for streaming data and dynamic environments.
Understanding Online Learning
Online learning is a machine learning paradigm where the model updates its parameters incrementally as each new data point or small batch arrives, rather than training on the entire dataset at once. This approach is essential for systems that must adapt to changing data distributions in real time, such as stock market prediction engines, recommendation systems that respond to shifting user preferences, and fraud detection models that must keep pace with evolving attack patterns. Online learning algorithms like stochastic gradient descent process one example at a time, making them memory-efficient and suitable for streaming data. The key challenge is balancing the ability to learn new patterns without catastrophically forgetting previously learned information. Online learning connects to concepts like reinforcement learning, where agents continuously update their policy based on environmental feedback.
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.