Linear Regression
In supervised machine learning, there
are generally solve two types of problems which is regression and
classification. Regression is used to predict the numeric value from historical
data.
In regression technique, our aim is to
find the best fit line for a given data set.
Regression is two type linear
regressions and multiple linear regression. In regression, we find out the
linear relationship between the input variable and the target variable.
Linear Regression: Linear Regression means predicting
scores of one variable from the scores of the second variable. The variable we
are predicting is called the dependent variable and is referred to
as Y. The variable we are basing our predictions is known as
the un depended (predictor) variable and is referred to as X.
In general, we find a relationship
between target variable and independent variable by best fit line.
The equation of a line is as:
Y= mx + c
Here
Y= depended variable (target
variable)
m = slope of a line (coefficient)
x = independent variable
c = constant
Multiple linear
regressions
The above equation can be used when we
have one input variable. However, in general, we usually deal with datasets
which have multiple input variables. The case when we have more than one
feature is known as multiple linear regressions, or simply, linear regression.
We can generalize our previous equation for simple linear regression to
multiple linear regressions:
Y(x) = w0 + w1x + w2x +………+Wnx
In the case
of multiple linear regression, instead of our prediction is a line in
2-dimensional space, it is a hyperplane in n-dimensional space. For example, in
3D, our plot would look as follows
The goal of the Hypothesis is to choose B0
and B1 so that Yi is close to Y for our training data while
choosing B0 and B1 we have to consider the cost function( J(θ) )
where we are getting low value for cost function( J(θ) ).
The below function is called a cost
function, the cost function ( J(θ) ) is nothing but just a Squared error
function.
In this post its the basic introduction about the linear regression if you are interested about the detail introduction please comment us.
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