Classification and Regression Evaluation Metrics — Part 1

//Classification and Regression Evaluation Metrics — Part 1

Classification and Regression Evaluation Metrics — Part 1

I have published an article Classification and Regression Evaluation Metrics — Part 1. In this Part1, some of the  classification evaluation metrics are explained.

Link

https://medium.com/@balamurali_m/classification-and-regression-evaluation-metrics-part-1-17e6efbe3bf4

The python code can be viewed at:

https://gist.github.com/balamurali-m/3fd696b1a3e53341a6442cb020c79de6

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.The best way to analyse any key concept or problem in machine learning is to code & implement and analyse the results. I have written the classification example in Python.  In this article Confusion matrix, True Negatives, False Positives, False Negatives, True Positives, sensitivity (true positive rate), specificity (true negative rate), accuracy, etc are explained

For details please refer the above links to the article.

You can also refer my blog to read this article:

Classification and Regression Evaluation Metrics 

2018-08-12T20:02:49+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/

Leave A Comment