Introduction To supervised Learning

Supervised learning is when the model is getting trained on a labelled dataset(label dataset is the dataset where both input and output data is available).

it is just like teacher teach a new student. supervisor is one dataset which has both input and output.

with this labelled dataset, machine is trained and a new data is given to model and the model take decission or predict the data.

example. we train the algo with input like 2+2 and 4 as labelled data , then we can predict with the new data for the addition

Supervised learning allgo is used to solve 2 type of problems:

1.Regression (deals with continuous and numeric value like stock,weather prediction)

2.Classification (deals with categorical value eg: to identity picture whether cat or dog, in which you classify the specific category)

 

To solve regression problems we have algorithms like

1. linear regression

2.multiple regression

3.polynomial regression

4.support vector regrssion

5.Decission tree regression

6.Random forest regression

 

To solve classification problems we have algorithms like:

1.Logistic regression

2.k-near Neighbour (KNN)

3.Support vector machine

4.Naive Bayesr

5.Decission Tree classification

6.Random forest classification