Supervised Learning

Seite 1

Regression Analysis :

Terminologies :

1. Target Variable - what we need to get also called Dependent variable

2. Independent Variables : this are the factors affecting the target variables also called predictor

3. Outliers - are the values set outside the expected range , should be avoid to not hamper the resul

4.

Multicollinearity: If the independent variables are highly correlated with each other than other variables, then such condition is called Multicollinearity. It should not be present in the dataset, because it creates problem while ranking the most affecting variable.

Underfitting and Overfitting: If our algorithm works well with the training dataset but not well with test dataset, then such problem is called Overfitting. And if our algorithm does not perform well even with training dataset, then such problem is called underfitting.

Types of Regression :

Linear Regression

Logistic Regression

Polynomial Regression

Support Vector Regression

Decision Tree Regression

Random Forest Regression

Ridge Regression

Lasso Regression

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Linear Regression :

Simple connection between the independent variable x and the dependent variable y.

if prediction can be done by single element then it is simple linear reg , else complex regression or multiple linear regression

Logistic Regression :

this is the category of classification problem , where output or dependent variable is dicrete quatites like false or true , 0 or 1

here applies the concept of probability , and uses a logistic or sigmoid function to determine the output ,

ltype of outputs :

binary ( 0,1)

multi (cats , dogs , cows)

ordinal ( low , medium , high)