![]() ![]() I've been experimenting with using the trusty old Binary Search to tune hyperparameters. Plus, then you've got hyper-hyperparameters to tune - how many iterations SHOULD you run it for, anyway? RandomizedSearchCV goes noticeably faster than a full GridSearchCV but it still takes a while - which can be rough, because in my experience you do still need to be iterative with it and experiment with different distributions. Frequently containing weird non-linearities in how changing a parameter changes the score and/or the time it takes to train the model. Using Pandas and SQLAlchemy to Simplify DatabasesĪh, hyperparameter tuning.Getting Conda Environments To Play Nicely With Cron.Trash Pandas: Messy, Convenient Database Operations via Pandas.Importing Excel Datetimes Into Pandas, Part I.Importing Excel Datetimes Into Pandas, Part II.Tuning Machine Learning Hyperparameters with Binary Search. ![]() Tuning Random Forests Hyperparameters: max_depth.Tuning Random Forests Hyperparameters: min_samples_leaf.Using Random Forests for Feature Selection with Categorical Features.Downcast Numerical Data Types with Pandas.Recasting Low-Cardinality Columns as Categoricals.Being REALLY Lazy With Multiple Aggregations in Pandas.
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