#### Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Python Machine Learning Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
The Realm of Supervised Learning
Visualizing Data
Index

## Computing regression accuracy

Now that we know how to build a regressor, it's important to understand how to evaluate the quality of a regressor as well. In this context, an error is defined as the difference between the actual value and the value that is predicted by the regressor.

Let's quickly understand what metrics can be used to measure the quality of a regressor. A regressor can be evaluated using many different metrics, such as the following:

• Mean absolute error: This is the average of absolute errors of all the datapoints in the given dataset.

• Mean squared error: This is the average of the squares of the errors of all the datapoints in the given dataset. It is one of the most popular metrics out there!

• Median absolute error: This is the median of all the errors in the given dataset. The main advantage of this metric is that it's robust to outliers. A single bad point in the test dataset wouldn't skew the entire error metric, as opposed to a mean error metric.

• Explained variance score: This score measures how well our model can account for the variation in our dataset. A score of 1.0 indicates that our model is perfect.

• R2 score: This is pronounced as R-squared, and this score refers to the coefficient of determination. This tells us how well the unknown samples will be predicted by our model. The best possible score is 1.0, and the values can be negative as well.

### How to do it…

There is a module in scikit-learn that provides functionalities to compute all the following metrics. Open a new Python file and add the following lines:

```import sklearn.metrics as sm

print "Mean absolute error =", round(sm.mean_absolute_error(y_test, y_test_pred), 2)
print "Mean squared error =", round(sm.mean_squared_error(y_test, y_test_pred), 2)
print "Median absolute error =", round(sm.median_absolute_error(y_test, y_test_pred), 2)
print "Explained variance score =", round(sm.explained_variance_score(y_test, y_test_pred), 2)
print "R2 score =", round(sm.r2_score(y_test, y_test_pred), 2)```

Keeping track of every single metric can get tedious, so we pick one or two metrics to evaluate our model. A good practice is to make sure that the mean squared error is low and the explained variance score is high.