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Simple Linear Questions and Answers

Q: What is a linear regression?

A: It is all about getting the best fit line that supports linearity. The relation between independent and dependent variables helps to form a  Straight Line.


Q: Assumptions of Linear regression?

A: a)The relation between independent and dependent variables that supports Linearity.

    b)Multicollinearity.The relation between one feature with other feature

    c)Homoscedasticity: It means the same distribution of error for all the independent variables.

   d)Hetrosedasticity: it is quite opposite to Homescedasticity.no Equal distribution of the errors.


Q: Regression problems evaluation metrics and which one we have to use?

A: In regression problems, we are having a different  type of evaluation metrics like RMSE,
MSE, MAE, etc. And now the question is which one we have to use?
for regression problems, we can use any metrics to calculate the score but in Compilation time we use MAE(mean absolute error), which saves execution time and memory space, etc So most of the people use this.

Q: OUTLIER treatment required for Linear regression?
A) Yes Removing Outliers will help to improve the score.

Q): if our feature not following any Gaussian Distribution what we have to do?

A) we have to use 1) Log Transformation or
                              2) Reciprocal Transformation or
                              3)Exponential Transformation or
                              4)Sqareroot Transformation 
you can choose any technique for your feature that falls in any of the standard distribution.

Q)Missing value treatment is required for Linear regression

A) Missing value treatment is required for all the models. fill missing values with (mean, mode, median)

Q) Advantages and disadvantages of  Linear Model
A)  
  1. It can handle overfitting using dimensionality reduction techniques and cross-validation and regularization.
  2. Linear regression performs exceptionally well for linearly separable data.
Disadvantages:
  1. Sometimes Lot of Feature Engineering Is required
  2. If the independent features are correlated it may affect performance
  3. It is often quite prone to noise and overfitting

Q) Linear regression require Feature scaling?

A) Yes it needs a feature scaling.

Q) Linear regression for Time-series analysis
A) yes you can use but the results not accurate.


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