---
title: "evaluation"
source: https://www.jemoka.com/posts/kbhmachine_learning_evaluation/
---

our ultimate goal is to create a generalized model that learns training data and extrapolate to future test data.
We don&rsquo;t really care about how good we fit the training data.
key idea: fit the model on train set, and test on separate test set.
requirements We split our training set into three parts
training set: to fit the model validation set: quasi-test set test set: actual test (we do it only once) additional information root-mean-square error this is basically least-squares error but with normalization
\begin{equation} \text{RMSE}\qty(\theta) = \sqrt{\frac{1}{n} \sum_{i=1}^{n} \qty(h_{\theta} \qty(x^{(i)}) - y^{(i)})^{2}} \end{equation}
we don&rsquo;t train with this because its like more faff but monotonic against least-squares error so there&rsquo;s no point in adding the more faff.
