# Linear, Higher and n-dimensional Space in Machine Learning

Usually refers to number of attributes.There are different types of dimension in which we can represent our data points.Linear,Higher,n-dimensional space.

## Dimensions

Usually refers to number of attributes. For more details on dimensions visit here.

There are different types of dimension in which we can represent our data points.

## Dimensions are of different types :

### Linear Dimensions (2D):

w1.x1 + w2.x2 + b = 0

w.x+b = 0

- w = (w1,w2)
- x
**=**(x1,x2) - y = label : 0 or 1
- b = bias

Prediction :

y̅ = '1' if wx+b >= '0' **or** '0' if wx+b<'0'

### Higher Dimensions(3D)

Line Equation : 2D

Plane : 3D

w1.x1+w2.x2+w3.x3+b=0

wx+b = 0

Prediction :

y̅ = '1' if wx+b >= '0' **or** '0' if wx+b<'0'

### n-Dimensional Space

x1,x2,x3,-------,xn

w1.x1+w2.x2+w3.x3+--—+wn.xn+b=0

Prediction :

y̅ = '1' if wx+b >= '0' **or** '0' if wx+b<'0'