Day 12 - How to find the best K value in K-Means Algorithm - Elbow Curve

-

          By Jerin Lalichan 


How to do it?

Clustering algorithms like K-Means need the user to input the number of clusters to be formed. For this, we need to find the optimum number of clusters to be generated. A commonly used method is using the Elbow Curve.


Elbow Curve / Knee Curve

    K means works in a way to reduce the Within-cluster sum of squares (WCSS) is minimized. In this method, we vary the value of K from 1 to 10. 

    For each value of K, the WCSS is calculated. WCSS is nothing but the sum of squares of the distance between each value and their corresponding cluster centroid. 




    We start with K=1, and the highest value for WCSS  is observed for K=1.
When the K goes higher, WCSS decreases. And from the above graph, we can see that WCSS shows a rapid change at a certain point (here K=5), and the line gets parallel to the X axis. And this point is called the Elbow point and is taken as the optimum value of K. 
    







   
  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!  





Comments

Post a Comment

Popular posts from this blog

Day 17 - Ensemble Techniques in ML - Averaging, Weighted average

-
   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.     F or 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 diversif
Read more

Day 4 - Performance metrics in Machine Learning - Regression

-
Image
      By  Jerin Lalichan  Performance metrics in ML     Evaluation of the performance of a model is important. Performance metrics are certain measures to quantify the performance of the model during the training and testing phases. In Machine learning, there are generally two kinds of performance metrics in use. For regression models and for classification models. Below are the most popular metrics in use: Regression Metrics Mean Squared Error (MSE) It is simply the average of the sum of the squares of the difference between the actual value and predicted values. Due to the squaring in this equation, small errors are overestimated. Also because for that reason, this is very much prone to outliers.  2 .  Mean Absolute Error (MAE) Mean Absolute Error is the average of the difference between the ground truth(actual value)  and the predicted values . Since there is no squaring, the error estimated is not exaggerated or overestimated. Also, it is better with outliers than MSE. Even though
Read more