Compare Machine Learning Algorithms
Algorithms were compared on OpenML datasets. There were 19 datasets with binary-classification, 7 datasets with multi-class classification, and 16 datasets with regression tasks. Algorithms were trained with AutoML mljar-supervised. They were trained with advanced feature engineering switched off, without ensembling. All models were trained with the 5-fold cross validation with shuffle and stratification (for classification tasks). Different hyperparameters (if available) for each algorithm were checked during the training.
For binary classification the Area Under ROC Curve (AUC) metric was used. For multi-class classification the LogLoss metric was used. The regression task was optimized with Root Mean Square Error (RMSE).