v1.4.2

• fixed handling of custom val_metric_name HPO models and Ensemble_TD_Regressor.
• If tmp_folder is specified in HPO models, save each model to disk immediately instead of holding all of them in memory. This can considerably reduce RAM/VRAM usage. In this case, pickled HPO models will still rely on the models stored in the tmp_folder.
• We now provide RealMLP_Ensemble_Classifier and RealMLP_Ensemble_Regressor, which will use weighted ensembling and usually perform better than HPO (but have slower inference time). We recommend using the new hpo_space_name='tabarena' for best results.