Classification and Regression Evaluation Metrics — Part 2

//Classification and Regression Evaluation Metrics — Part 2

Classification and Regression Evaluation Metrics — Part 2

I have published an article Classification and Regression Evaluation Metrics — Part 2. In this Part2, 2 of the  regression evaluation metrics are explained – the Mean Absolute Error (MAE) and Mean Squared Error (MSE).

Link

https://medium.com/@balamurali_m/classification-and-regression-evaluation-metrics-part-2-bd5888876c4e

The python code can be viewed at:

https://gist.github.com/balamurali-m/34fbfa2726090841b173899e7d972e48

We need to evaluate our machine learning algorithms with the help of various metrics. There are some commonly used metrics for regression and classification problems. We will see cover some of these evaluation error metrics. We will take a look at two regression evaluation metrics — MAE (Mean Absolute Error) and MSE (Mean Squared Error). I have coded the regression example in Python. For details please refer the above link to the article.

You can also refer to my blog for the article.

Classification and Regression Evaluation Metrics 

2018-08-12T20:01:23+00:00

About the Author:

Balamurali M
Hello I am Balamurali M. My areas of interest are Data Science, Machine Learning, Statistics, Management, Business analytics and Mathematics. I can be reached at: Twitter : https://twitter.com/Balamu_M , LinkedIn : https://www.linkedin.com/in/balamurali-m-43b022168/

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