*This is part of my notes taken while studying machine learning. I’m learning as I go along, and may update these with corrections and additional info.*

In my Linear Regression for ML notes, we discussed need way to move the line to best fit our data. Two methods for doing so are:

- Absolute Trick
- Square Trick

### The Absolute Trick

The Absolute Trick is a method for moving the line closer to a point, and looks like this:

*y = (w1 + p⍺)x + (w2 + ⍺)*

p = the horizontal distance to the point

⍺ = (pronounced alpha) is what is referred to as the learning rate

⍺,the learning rate, is a small number whose sign (+ or -) depends on if the point is above or below the line.

### Absolute Trick Example

In this example, our line is currently at y = 2x + 3 and we want to move it closer to the point at (5, 15).

With a learning rate of 0.1, we are going to use the absolute trick expression:

*y = 2x + 3* starting expression

*y = (w _{1} + p⍺)x + (w_{2} + ⍺) *move it to this

*y = (2 + (5 x 0.1))x + (3 + 0.1)* add our learning rate

*y = 2.5x + 3.1* and now we have this.

When we plot our new expression, the line is a lot closer to our point:

Next up, we have the Square Trick

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