This page lists down the practice tests / interview questions for Linear (Univariate / Simple Linear) / Multiple (Multilinear / Multivariate) regression in machine learning. Those wanting to test their machine learning knowledge in relation with linear/multi-linear regression would find the test useful enough. The goal for these practice tests is to help you check your knowledge in numeric regression machine learning models from time-to-time. More importantly, when you are preparing for interviews, these practice tests are intended to be handy enough. Those going for freshers / intern interviews in the area of machine learning would also find these practice tests / interview questions to be very helpful.
This test primarily focus on following some of the following concepts related with linear regression models:
- Degrees of freedom
- Concepts related with Sum of Squares Total (SST), Sum of Squares Regression (SSR) and Sum of Squares Error (SSE)
Linear Regression – Measures of Variation
The diagram below represents measures of variation in terms of following:
- Actual value of dependent variable
- Predicted value of dependent variable (best fit line)
- Average value of dependent variable assuming the value of all coefficients is zero
Pay attention to some of the following in above diagram:
- Sum of squares total (SST) is sum of squares of actual value (Y) minus the average value of dependent variable (Y bar)
- Sum of squares total (SSE) is sum of squares of actual value (Y) minus the predicted value as per the best fit line (Y^)
- Sum of squares total (SSR) is sum of squares of predicted value as per the best fit line (Y^) minus average value of dependent variable (Y bar)
SST = SSR (variability due to regression) + SSE (Unexplained variability) Value of R-Squared = SSR / SST
The objective is to maximise SSR and minimize SSE.
Other tests in this series includes some of the following:
- Linear, Multiple Regression Interview Questions Set 1
- Linear, Multiple Regression Interview Questions Set 2
- Linear, Multiple Regression Interview Questions Set 3
Practice Test
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In case you have not scored good enough, it may be good idea to go through basic machine learning concepts in relation with linear / multi-linear regression. Following is the list of some good courses / pages:
- Confounder Features & Machine Learning Models: Examples - October 2, 2024
- Credit Card Fraud Detection & Machine Learning - September 26, 2024
- Neural Network Types & Real-life Examples - September 24, 2024
I found it very helpful. However the differences are not too understandable for me