> For the complete documentation index, see [llms.txt](https://gitbook.nicacton.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gitbook.nicacton.com/software-engineering/machine-learning/training-and-loss.md).

# Training and Loss

Training a model just means reducing **loss** in a process called **empirical risk minimization**. **Loss** is a number indicating how bad your model predicted when compared to a single known example.

### Squared Loss

Also **L2 loss**

```
= the square of the difference between the label and the prediction
= (observation - prediction(x))2
= (y - y')2
```

Also **Mean squared error (MSE)** 

$$
MSE = 1/N \sum(y-prediction(x))^2
$$

Where:\
*(x,y)* is an example in which x is the set of features and y is the label\
*prediction(x)* is a function of the weights and bias in combination with the set of features *x*\
*D* is a data set containing many labeled examples, which are *(x,y)* pairs\
*N* is the number of examples in *D*