Hyperparameter
Configuration settings set before training that control the learning process, not learned from data.
In-depth explanation
Hyperparameters are external configurations that affect how a model learns. Unlike model parameters (learned during training), hyperparameters are set by the practitioner. Examples include learning rate, number of layers, regularization strength, and batch size. Hyperparameter tuning-finding optimal values-significantly impacts model performance.
Examples
Related terms
More in Machine Learning
Supervised Learning
Machine learning approach where models learn from labeled training data to predict outcomes.
Unsupervised Learning
Machine learning approach where models find patterns in data without labeled examples.
Semi-Supervised Learning
Machine learning approach using a small amount of labeled data with a large amount of unlabeled data.
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Regression
Predicting a continuous numerical value based on input features.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
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