In this post we will learn how to learn Linear Regression Machine Learning Algorithms for data science interview. Here I will be discussing about the important interview questions.
The first thing is that you need to be very good at the theoretical understanding so that you will able to explain how the machine learning algorithm works. You can check the video below to get the proper intuition of the algorithm.
1. What Are the Basic Assumption?(favourite)
There are four assumptions associated with a linear regression model:
- Linearity: The relationship between X and the mean of Y is linear.
- Homoscedasticity: The variance of residual is the same for any value of X.
- Independence: Observations are independent of each other.
- Normality: For any fixed value of X, Y is normally distributed.
- Linear regression performs exceptionally well for linearly separable data
- Easy to implement and train the model
- It can handle overfitting using dimensionlity reduction techniques and cross validation and regularization
- Sometimes Lot of Feature Engineering Is required
- If the independent features are correlated it may affect performance
- It is often quite prone to noise and overfitting
4. Whether Feature Scaling is required?
5. Impact of Missing Values?
It is sensitive to missing values
6. Impact of outliers?
linear regression needs the relationship between the independent and dependent variables to be linear. It is also important to check for outliers since linear regression is sensitive to outlier effects
7. Interview Question on Multicollinearity
8. Practical Solution On Multicollinearity
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