Use Voting Classifier to improve the performance of your ML model

You never know if your model is useful unless you evaluate the performance of the machine learning model. The goal of a data scientist is to train a robust and high-performing model. There are various techniques or hacks to improve the performance of the model, ensembling of models being one of them. Ensembling is a powerful technique to improve the performance of the model by combining various base models in order to produce an optimal and robust model. In this article, we will discuss the implementation of a voting classifier and further discuss how can it be used to improve the performance of the model.