Overfitting
Overfitting occurs when a model learns the training data too well, including its noise and outliers, and fails to generalize to new data. Regularization, dropout, and early stopping are common strategies to combat overfitting.
Understanding Overfitting
Overfitting occurs when a machine learning model learns the training data too well, capturing noise and idiosyncratic patterns rather than the underlying generalizable relationships, resulting in poor performance on new unseen data. A model that achieves near-perfect accuracy on training examples but fails dramatically on a test set is a classic sign of overfitting. The problem is especially prevalent when models have too many parameters relative to the amount of training data available. Practitioners combat overfitting through regularization techniques like L1 and L2 penalties, dropout layers in neural networks, early stopping during training, and data augmentation to artificially expand the training set. Cross-validation provides a robust method for detecting overfitting by evaluating model performance across multiple data splits. Understanding the bias-variance tradeoff is essential to balancing model complexity against the risk of overfitting.
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.