Hyperparameter Tuning
Hyperparameter tuning is the process of finding optimal hyperparameter values to maximize model performance. Methods include grid search, random search, and Bayesian optimization.
Understanding Hyperparameter Tuning
Hyperparameter tuning is the systematic process of finding the optimal set of hyperparameters that maximize a machine learning model's performance on validation data. Common approaches include grid search, which exhaustively tests all combinations in a defined space; random search, which samples combinations randomly and is often more efficient; and Bayesian optimization, which uses probabilistic models to intelligently select the next set of values to evaluate. More advanced methods like population-based training and Hyperband combine early stopping with resource allocation to accelerate the search. Hyperparameter tuning is a computationally expensive but essential step in the machine learning pipeline, often making the difference between a mediocre and a state-of-the-art model. Tools like Optuna, Ray Tune, and Weights & Biases help automate and track tuning experiments.
Category
Machine Learning
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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
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Bagging
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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.