Regularization
Regularization is a set of techniques that prevent overfitting by adding constraints or penalties to the model during training. Common methods include L1/L2 regularization, dropout, and early stopping.
Understanding Regularization
Regularization encompasses techniques that constrain or penalize a machine learning model's complexity to prevent overfitting and improve generalization to unseen data. Common approaches include L1 regularization (Lasso), which encourages sparse models by penalizing the absolute value of parameters, and L2 regularization (Ridge), which penalizes large parameter magnitudes. In deep learning, dropout randomly deactivates neurons during training, forcing the network to develop redundant representations, while early stopping halts training before the model begins memorizing noise. Data augmentation serves as an implicit regularizer by artificially expanding the training set through transformations like cropping, rotating, and adding noise. Weight decay, batch normalization, and label smoothing are additional regularization strategies widely used in modern neural network training. The choice and strength of regularization directly impact the bias-variance tradeoff, making it one of the most important considerations when designing machine learning systems.
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.