Hyperparameter optimization: Difference between revisions

Revision as of 13:15, 19 January 2018

Revision as of 15:44, 4 February 2018

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# Create an initial population of random solutions (i.e., randomly generate tuples of hyperparameters, typically 100+)

# Evaluate the hyperparameters tuples and acquire their [[fitness|fitness function]] (e.g., 10-fold [[Cross-validation (statistics)|cross-validation]] accuracy of the machine learning algorithm with those hyperparameters)

# Rank the hyperparameter tuples by their relative fitness

# Replace the worst-performing hyperparameter tuples with new hyperparameter tuples generated through [[crossover (genetic algorithm)|crossover]] and [[mutation (genetic algorithm)|mutation]]