# 3-2: Regression

This lesson covers more in-depth topics for **supervised regression learning**.

## Parametric regression

**Parametric regression** leverages **parameters** in a given **model** to
predict the outcome given some **observations**. In the example provided below,
`x`

is the observation of barometric pressure, the polynomial equation is the
model generated by some machine learning algorithm to predict how much it's
going to rain. The **parameters** in this case are the slope of the line, `m`

,
and the constant `b`

.

## K nearest neighbor

We use the value `k`

to select the nearest neighbor(s) for a historical data
point to make a prediction. The slides from the lecture, below, demonstrate how,
for a given query, we use this algorithm to select 3 of our nearest neighbors to
predict the amount of rain given historical data. We take the **mean** of the
nearest neighbors to generate our prediction.

## Training and testing

This section of the lecture covers how we treat our data for **training** and
**testing**. Our **training** data should be separate from our **testing** data.
The **training** data should be used with some machine learning algorithm,
parametric or KNN, to generate a model. Once the model is generated, we can then
use the **testing** data to make predictions.