April 2018
Beginner to intermediate
566 pages
12h 17m
English
In this section, we will understand the testing matrix and visualization approaches to evaluate the performance of the trained ML model. So let's understand both approaches, which are as follows:
We are using the default score API of scikit-learn to check how well the ML is performing. In this application, the score function is the coefficient of the sum of the squared error. It is also called the coefficient of R2, which is defined by the following equation:
Here, u indicates the residual sum of squares. The equation for u is as follows: ...