tuning redone

model_selection/hyperparam_tuning.py

@@ -155,7 +155,7 @@ if __name__ == "__main__":
                 print(f"{group}-{method_names[j]}-{model_name}")
                 # Find optimal hyperparams for curr model
                 params = hyperparameters[model_name]
-                search = RandomizedSearchCV(model, param_distributions=params, cv=cv, n_jobs=8, scoring='precision')
+                search = RandomizedSearchCV(model, param_distributions=params, cv=cv, n_jobs=10, scoring='precision')
                 search.fit(X,y)
                 hyperparam_df.at[model_name,'Parameters']=search.best_params_
                 hyperparam_df.at[model_name,'Score']=round(search.best_score_,4)