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Two useful modules to help you find the best ML model for your task | Oxford Protein Informatics Group

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FLAML and LazyPredict are two packages designed to quickly train and test machine learning models from scikit-learn so that you can determine which is the best type of model for learning from your data. Both are easily pip-installable, and require only a few lines of code. When training for classification, all you need for LazyPredict is : while for FLAML, this is all that is needed: While their ideas are very similar, there are a few key differences:

FLAML and LazyPredict are two packages designed to quickly train and test machine learning models from scikit-learn so that you can determine which is the best type of model for learning from your data. Both are easily pip-installable, and require only a few lines of code. When training for classification, all you need for LazyPredict is : from lazypredict.Supervised import LazyClassifier clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None) models,predictions = clf.fit(X_train, X_test, y_train, y_test) while for FLAML, this is all that is needed: from flaml import AutoML au

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