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# Question Categories: Supervised Learning

Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.

## Dropping of variables which are not significant and are having high VIF value

For more accuracy while fitting a linear model, we drop the input variables/features which are not significant(i.e pvalue>0.05) and whose variation inflation factor is greater than 5,I have landed up in a case where both input variables are highly correlated with each other and significant too,so which one should I drop?

## Scaling for numeric variables

Why is scaling for numeric variables done before splitting it into a train & test dataset?

## Intercept in linear regression model

Why do we need an intercept in a linear regression model? If we use statsmodel.OLS do we need to add the intercept explicitly and how do we do it?

## Linear regression with multiple variables

I was working on Dataset of Insurance in Python it had both categorical and numeric variables. For fitting a linear regression model I did the conversion of categorical to dummy variable, did scaling of whole data frame afterwards, after splitting training and testing data and, fitting model on training data, I found that VIF of… Continue reading Linear regression with multiple variables

## Constant in Logistic Regression

Why do we need to add a constants column with the independent variables while doing logistic regression?

## P-Value in Linear Regression

In Linear Regression, if the p-value of the f-stat is < 0.05 we reject the H0 and accept the Ha. Which means we know that at least one variable has a coefficient greater than zero. For individual variables, we consider the variable to be statistically significant if the p-value<0.05. Why this is so?

## Model Overfitting or Underfitting

How do I know if my model is Overfitting or Underfitting?

## Model Export

Is it possible export or save a model so that whenever i want i can run the model if so how should i do it?