Day 16 - Ensemble Techniques in ML - Max Voting

 By Jerin Lalichan 

Ensemble Techniques in ML

    Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model.

    Ensemble methods include building multiple models and combining them to achieve better outcomes. To put it another way, they integrate the conclusions drawn from various models to enhance overall performance. Generally speaking, ensemble methods produce more accurate results than a single model would.

    For example, let's consider the case in which you need to decide if you should go to a particular movie or not. You can infer that a diverse group of people are likely to make better decisions as compared to individuals. So it's best to check online reviews since it is an aggregation of reviews of hundreds of people from different backgrounds when compared to asking a few of your friends. 

    

    Similar is true for a diverse set of models in comparison to single models. This diversification in Machine Learning is achieved by a technique called the Ensemble technique.

Simple Ensemble Techniques

1. Max Voting
   
   
2. Averaging
   
   
3. Weighted Averaging

Max Voting

    For classification problems, the max voting method is generally used. With this method, predictions are made for each data point using a variety of models. Each model's predictions are considered a "vote." The majority of the models' forecasts serve as the basis for the final prediction.

    For example, when you asked 7 of your colleagues to rate a movie (out of 10); we’ll assume Four of them rated it as 8 while two of them gave it a 5 and the rest of them as 3. Since the majority gave a rating of 8, the final rating will be taken as 8. You can consider this as taking the mode of all the predictions.

   

  I am doing a challenge - #66DaysofData  in which I will be learning something new from the Data Science field for 66 days, and I will be posting daily topics on my LinkedIn, On my GitHub repository, and on my blog as well.

Stay Curious!  

By Jerin Lalichan