20 Add preprocessing for new data in case of missing values not present in train data.
0.1.5
fix tqdm on jupyter
0.1.4
Add missing requirements in setup.py
0.1.3
- set metric to be optimized (17) - create table with model details (8) - progress bar for training (9) - add reproducibility tests (5) - callback to control number of iterations (11) - fixed: set path for catboost snapshot (16) - learning curves (14)
0.1.2
The autoML predicts categorical labels as addition to probabilities. There is an optimal threshold computed for the best model which maximize F1 score.
The predicted data frame right now looks like this:
p_0, p_1, label
0.1.1alpha
The AutoML solution that can solve binary classification tasks with respect to LogLoss metric. There are used following algorithms: - Random Forest - CatBoost - LightGBM - Xgboost - Neural Networks