Meta-model Analysis - Model Stacking
Meta-model analysis was carried out to see if the different models predict better on the different feature spaces of the test data set. Model outputs were trained as inputs into gradient boosting models (Catboost, LGBM and ExtraTreesClassifiers).
The CatBoost meta-model accuracy (56%) improved on the best accuarcy score (55%) and went on to outperform the SentiMetre Model 2 ranked by return.
Accuracy tr_pred 0.547798066595059 Accuracy catboost_pred 0.5596133190118152 Accuracy lgbm_pred 0.5528105979233798 Accuracy xtrees_pred 0.573218761188686