WeightedEnsemble 1.0.0

will help in getting the correct classification model

Classification.Weighted_Ensemble(Models,X_train,y_train,X_val,y_val,X_test,y_test,Method=’SLSQP’)

Any classification model or an average Ensembling model combines the prediction from each model equally and often results in better performance on average than a given single model.

Weighted Ensemble model is an approach that allows multiple models to contribute to a prediction in proportion to their trust or estimated performance.

Using this above mentoined Weighted_Ensemble class You can predict single model as well as multiple models Which will give the clarity about the weightage of the model with respect to inputs

Parameters: Models==List of the model objects that you wanted to Predict X_train==Traininig dataset y_train==Training label dataset x_val==Validation data set Y_val==Validation labels dataset #X_val and y_Val are used to calculate the weights of the model objects that are there in the Models List NOte:The sum of the weights of the modes mentioned in the object are always equal to 1 x_test==Testing dataset to calculate the metric score Y_test==Testing label dataset to calculate the metric score

Example in the code declare models=[] Model1: rf=Randomclassifier() models.append(rf)

Model2: xgb=XGBClassifier() models.append(xgb)

result,weights=Weighted_Ensemble(Models,X_train,y_train,X_val,y_val,X_test,y_test,Method=’SLSQP’)

Result is a data frame which is something like below threshold tp fp tn fn accuracy precision recall scope f1 fpr Threshold values range from o.1 to 0.99 Weights are the list of values that shows how much weightage it has given to the model We can compare the values and pick the model very easily

Classification.get_metric_score(y_true, y_proba): If we have model already in hand we can call this method to observe the perfomance of the model with various thershold values Result is a data frame which is something like below threshold tp fp tn fn accuracy precision recall scope f1 fpr Threshold values range from o.1 to 0.99

Parameters: y_true==actual y values y_proba==We should give the probability values with respect to the predcition once

Example: y_true is the actual values Y_proba=np.round(model.predict_proba(X_test)[:,1],3) result_df=get_metric_score(y_true, y_proba)