0.10.2

Add support to Python 3.9 (#339) Thanks to @rterbush!

0.10.1

Enhancements

• #332 We added Optuna framework for hyperparameters tuning. It can be used by setting mode="Optuna" in AutoML. You can read more details at blog post: https://mljar.com/blog/automl-optuna/

0.9.1

Enhancements

• #179 add need_retrain() method to detect performance decrease
• #226 extract rules from decision tree
• #310 add support for MAPE
• #312 optimize prediction time
• #313 set stacking time threshold depending on best model train time
• #320 search for model with prediction time constraint
• #322 n_jobs as a parameter
• #328 disable stacking for small (nrows < 500) datasets

Bug fixes

• #214 move directory after training
• #246 raise exception when small time limit and no models are trained
• #247 proper display for optimize AUC and R2
• #306 add mix_encoding argument in AutoML constructor
• #308 fix dependencies error in kaggle notebook
• #314 bug fix in hill climbing in Perform mode
• #323 fix catboost bug with tree limit
• #324 #325 bug for feature importance for small data

0.8.8

Many small improvements.

0.8.4

A lot of small tweaks and improvements :)

0.8.0

Enhancements

• #300 Add step with k-means additional features
• #299 Add Boost On Errors step
• #154 Sample weight available
• #229 Sort leaderboard (disabled for now for debug purposes)

Bug fixes

• #301 Fix storing unique keys in mljar tuner only for trained models
• #275 #248 small fixes

0.7.19

Bug fixes

• #293 Typo in is_scale_needed
• #277 Fix problem with unit data
• #285 Restricted characters in LightGBM

0.7.18

Bug fixes

• #292 Remove unused params from CatBoost

0.7.17

Enhancements

• #291 Disable loo encoding
• #290 improve ordering in hill climbing
• #287 replace mix_encoding with integet_encoding

0.7.16

Bug fixes

• #283 Don use Random Feature model

Enhancements

• #284 Check time for features selection
• #286 Add R2 score
• #288 Improve algorithms order in not_so_random step