*This article was written by Jason Brownlee. Jason is the editor-in-chief at MachineLearningMastery.com.He has a Masters and PhD in Artificial Intelligence, has published books on Machine Learning and has written operational code that is running in production.*

After you make predictions, you need to know if they are any good.

There are standard measures that we can use to summarize how good a set of predictions actually are.

Knowing how good a set of predictions is, allows you to make estimates about how good a given machine learning model of your problem,

In this tutorial, you will discover how to implement four standard prediction evaluation metrics from scratch in Python.

After reading this tutorial, you will know:

- How to implement classification accuracy.
- How to implement and interpret a confusion matrix.
- How to implement mean absolute error for regression.
- How to implement root mean squared error for regression.

Let’s get started.

You must estimate the quality of a set of predictions when training a machine learning model.

Performance metrics like classification accuracy and root mean squared error can give you a clear objective idea of how good a set of predictions is, and in turn how good the model is that generated them.

This is important as it allows you to tell the difference and select among:

- Different transforms of the data used to train the same machine learning model.
- Different machine learning models trained on the same data.
- Different configurations for a machine learning model trained on the same data.

As such, performance metrics are a required building block in implementing machine learning algorithms from scratch.

This tutorial is divided into 4 parts:

- 1. Classification Accuracy.
- 2. Confusion Matrix.
- 3. Mean Absolute Error.
- 4. Root Mean Squared Error.

These steps will provide the foundations you need to handle evaluating predictions made by machine learning algorithms.

To check out all this information, click here.

**Top DSC Resources**

- Article: What is Data Science? 24 Fundamental Articles Answering This Question
- Article: Hitchhiker's Guide to Data Science, Machine Learning, R, Python
- Tutorial: Data Science Cheat Sheet
- Tutorial: How to Become a Data Scientist - On Your Own
- Categories: Data Science - Machine Learning - AI - IoT - Deep Learning
- Tools: Hadoop - DataViZ - Python - R - SQL - Excel
- Techniques: Clustering - Regression - SVM - Neural Nets - Ensembles - Decision Trees
- Links: Cheat Sheets - Books - Events - Webinars - Tutorials - Training - News - Jobs
- Links: Announcements - Salary Surveys - Data Sets - Certification - RSS Feeds - About Us
- Newsletter: Sign-up - Past Editions - Members-Only Section - Content Search - For Bloggers
- DSC on: Ning - Twitter - LinkedIn - Facebook - GooglePlus

Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge

© 2019 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central