# 3-1: How machine learning is used at a hedge fund

## The ML problem

Plenty of hedge funds leverage machine learning models and just plain models
to make predictions about the market using observation. And this essentially
what a **model** does, machine learning or not. Provided some **observation**,
a **model** produces some **prediction**.

In this course, we'll cover how we can process large amounts of **data**,
provide it to a **machine learning algorithm** to produce a **model**, and use
that **model** to make **predictions** from provided **observations**.

## Choosing X and Y

Some examples provided by the course lecture to classify **observations** and
**predications** are as follows:

- observations
- price momentum
- Bollinger value
- current price

- predictions
- future price
- future return

## Supervised regression learning

What's the definition of **supervised regression learning**? **Supervised**
means we provided an example **observation** and **prediction**. **Regression**
means the model will be producing some **numerical prediction**. **Learning**
means we train the model with some data. There are multiple algorithms to
conduct **supervised regression learning**:

- Linear regression (parametric)
- Leverage data to create parameters and then discards the data

- K nearest neighbor (KNN) (instance based)
- Retains historic data and consults the data

- Decision trees
- Decision forests

## Backtesting

**Backtesting** is a technique wherein we utilize historical data with our
machine learning algorithm and subsequent model to make predictions on events
that have already happened. Using the results of our forecasting, we can make
determinations as to how accurate our model is and how confident we can be in
its predictions. Below is a slide from the lectures on this: