Interview Questions on Logistic Regression and Linear Regression

Instructor: Applied AI Course Duration: 30 mins

Revison  Questions

  1. Explain about Logistic regression?(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/geometric-intuition-1/)
  2. What is Sigmoid function & Squashing ?(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/sigmoid-function-squashing-1/)
  3. Explain about Optimization problem in logistic regression. (https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/mathematical-formulation-of-objective-function-1/)
  4. Expalain Importance of Weight vector in logistic regression.(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/weight-vector-1/)
  5. L2 Regularization: Overfitting and Underfitting.(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/l2-regularization-overfitting-and-underfitting/)
  6. L1 regularization and sparsity. (https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/l1-regularization-and-sparsity/)
  7. What is Probabilistic Interpretation: Gaussian Naive Bayes ?(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/probabilistic-interpretation-gaussian-naive-bayes-1/)
  8. Explain about Hyperparameter search: Grid Search and Random Search ?(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/hyperparameter-search-grid-search-and-random-search/)
  9. What is Column Standardization.?(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/column-standardization/)
  10. Explain about Collinearity of features?(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/collinearity-of-features-1/)
  11. Find Train & Run time space and time complexity of Logistic regression?(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/testrun-time-space-and-time-complexity-1/)

Self Learning:

  1. After analyzing the model, your manager has informed that your regression model is suffering from multicollinearity. How would you check if he’s true? Without losing any information, can you still build a better model?(Self Learning)
  2. What are the basic assumptions to be made for linear regression?(Self Learning)
  3. What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?(Self Learning)
  4. When would you use GD over SDG, and vice-versa?(Self Learning)
  5. How do you decide whether your linear regression model fits the data?(Self Learning)
  6. Is it possible to perform logistic regression with Microsoft Excel?(Self Learning)
  7. What is logistic regression? Or State an example when you have used logistic regression recently.(Self learning)
  8. When will you use classification over regression?(Self Learning)
  9. Explain the tradeoff between bias and variance in a regression problem.(Self Learning)

External Resources:

1.https://www.analyticsvidhya.com/blog/2017/08/skilltest-logistic-regression/

2.https://www.listendata.com/2017/03/predictive-modeling-interview-questions.html

3.https://www.analyticsvidhya.com/blog/2017/07/30-questions-to-test-a-data-scientist-on-linear-regression/

4.https://www.analyticsvidhya.com/blog/2016/12/45-questions-to-test-a-data-scientist-on-regression-skill-test-regression-solution/

5. https://www.listendata.com/2018/03/regression-analysis.html

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