Understanding the testing matrix
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:
The default testing matrix
The visualization approach
The default testing matrix
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:
The variable v indicates the total sum of squares. The equation for v is as follows:
The best possible score is 1.0, and it can be a negative score as well. A negative score indicates that the trained model can be arbitrarily worse. A constant model that always predicts the expected value for label y, disregarding the input features, will produce an...
