Two useful modules to help you find the best ML model for your task | Oxford Protein Informatics Group
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
related reading
- Parsed | Custom, interpretable AI systems that continuously learnparsed.com
- AI Model & API Providers Analysis | Artificial Analysisartificialanalysis.ai
- Goodfire AIgoodfire.ai
- CodaLab Worksheetsworksheets.codalab.org
- Replicate - Run AI with an APIreplicate.com
- Datacurve | The data engine for frontier AIdatacurve.ai
- Kaggle Solutionsfarid.one
- MAI-Thinking-1: Building a Hill-Climbing Machinemicrosoft.ai
- The ML drug discovery startup trying really, really hard to not cheat (Leash Bio)owlposting.com
- Hugging Face – The AI community building the future.huggingface.co
- ML Contestsmlcontests.com
- Data lab to train frontier models & evaluate agents | micro1micro1.ai