Machine Learning
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Feature Selection
Quick Definition
Identifying the most relevant features for model training
Full Definition
Identifying the most relevant features from a dataset for use in model training.
Examples
recursive feature elimination, mutual information, L1 regularization
Related Terms
feature-engineering
dimensionality-reduction
More Machine Learning Terms
Boosting
Sequentially combining weak learners correcting previous errors
Bias-Variance Tradeoff
The tension between model simplicity and complexity errors
Bayesian Optimization
Optimizing expensive functions using a probabilistic model
Clustering
Grouping similar data points based on feature similarity
Learning Rate
Hyperparameter controlling gradient descent step size
Dropout
Regularization randomly deactivating neurons during training