Machine learning is a highly quantitative field. Although we can gauge the performance of a model by plotting how it separates classes and how closely it follows data, more quantitative performance measures are needed in order to evaluate models. In this section, we present cost functions and metrics. Both of them are used in order to assess a model's performance.
Performance measures
Cost functions
A machine learning model's objective is to model our dataset. In order to assess each model's performance, we define an objective function. These functions usually express a cost, or how far from perfect a model is. These cost functions usually utilize a loss function to assess how well the model performed on each...
