Hyperparameter
A hyperparameter is a configuration value set before training that controls the learning process, such as learning rate, batch size, or number of layers. Unlike model parameters, hyperparameters are not learned from data.
Understanding Hyperparameter
A hyperparameter is a configuration value set before training begins that controls the behavior of a machine learning algorithm rather than being learned from data. Common hyperparameters include learning rate, batch size, number of hidden layers, dropout rate, and regularization strength. Unlike model parameters such as weights and biases, hyperparameters are not updated through backpropagation and must be specified by the practitioner or found through hyperparameter tuning. Poor hyperparameter choices can lead to underfitting, overfitting, or extremely slow convergence. The distinction between hyperparameters and learned parameters is fundamental to understanding how machine learning models are configured, and selecting appropriate values requires domain knowledge, experimentation, or automated search methods like grid search and Bayesian optimization.
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.