Regression
Regression models are
used to predict a continuous value. Predicting prices of a house given the features
of house like size, price etc. is one of
the common examples of regression. It is a supervised technique.
Types of Regression:-
- Simple
Linear Regression
- Polynomial regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression
1.
Simple
Linear Regression:- This si one of most common and interesting type of
regression technique. Here we predict a target variable Y based on the input
variable X. A linear relationship should exist between target variable and
predictor and so comes the name linear regression.
2.
Polynomial
Regression:- In polynomial regression,
we transform the original features into polynomial features of a given degree
and then apply linear regression on it.
3.
Support
Vector Regression:- In SVR, we identify a
hyper plane with maximum margin such that maximum numbers of data points are
within that margin. SVRs are almost similar to SVM classification algorithm.
4.
Decision
Tree Regression :- Decision tree can be
used for classification as well as regression. In decision trees, at each level
we need to identify the splitting attribute.
5.
Random
Forest Regression:- Random forest is an
ensemble approach where we take into account the predictions of several
decision regression trees.
1)
Select k
random points.
2)
Identify n
where n is the number of decision tree regressors to be created. Repeat step 1
and 2 to create several regression trees.
3)
The
average of each branch is assigned to leaf node in each decision tree.
4)
To predict
output for a variable, the average of all the predictions of all decision trees
are taken into consideration.
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