Learning about learning
At their heart, ML algorithms all contain one key feature: an optimization of some kind. The fact that these algorithms learn (meaning that they iteratively improve their performance concerning an appropriate metric upon exposure to more observations) is what makes them so powerful and exciting. This process of learning is what we refer to when we say training.
In this section, we will cover the key concepts underpinning training, the options we can select in our code, and what these mean for the potential performance and capabilities of our training system.
Defining the target
We have just stated that training is an optimization, but what exactly are we optimizing? Let's consider supervised learning. In training, we provide the labels or values that we would want to predict for the given feature so that the algorithms can learn the relationship between the features and the target. To optimize the internal parameters of the algorithm during training...
