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depshad / Machine-Learning-Interviews

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Notes

Colection of resources to revise topics in Machine Learning.

https://sebastianraschka.com/faq/

Data Engineering: https://docs.google.com/spreadsheets/d/1GOO4s1NcxCR8a44F0XnsErz5rYDxNbHAHznu4pJMRkw/edit#gid=0

STATS:

  • Estimate the minimum sample size for hypothesis testing
  • Linear regression model, varaible has low coefficient value, p<0.05, High Sum of squared error (ANOVA ?) contribution for the variale. What will you conclude
  • Hypothesis testing for 1) skewed data 2) proportions data with non normal distribution.
  • Categorical encoding methods. How to prevent overfitting for target encoding

Regression:

  1. What are the assumptions of linear regression? (Delhivery, ANZ bank, Citi Bank, Accenture )
  2. What is the meaning of multicollinearity? (ANZ, Amazon)
  3. How to detect multicollinearity? (Amazon, Delhivery)
  4. What do you understand by VIF (Variance Inflation Factor)? (Amazon)
  5. What is the difference between R-squared and adjusted R-squared? (Delhivery, ANZ bank, Citi Bank, Accenture)
  6. How to deal with multicollinearity in data? (Citi, Accenture)
  7. Explain forward and backward elimination? (Accenture)
  8. How does PCA work? (ICICI securities, Amazon, Miko.ai)
  9. Explain Ridge and Lasso Regression? (Delhivery, ANZ bank, Citi Bank, Accenture, Amazon)
  10. Can SVM be used for regression? (Miko.ai)
  11. What is the curse of dimensionality? Can you give an example?
  12. What is the difference between the coefficient of determination and coefficient of correlation?
  13. Give methods of variable selection in Regression Analysis? (Delhivery, ANZ bank, ICICI securities)
  14. Why do we perform the residual analysis? (ANZ)
  15. What are L1 and L2 penalization? (Miko.ai)
  16. What is heteroscadasticity? How does it affect the regression coefficients? (ANZ)
  17. Why does only VIF>10 implies that there is multicollinearity, why not choose vif>8? (IDFC First Bank)
  18. In my dataset, if I have 100 observations and 1500 features, do you think I would be able to fit the regression model onto that or not? (IDFC First Bank)
  19. For a single variable, how will you detect outliers? (ICICI Lombard)
  20. How correlation between two variables will change in the presence of an outlier? Will it increase, decrease or remain constant? Explain how, using its formula. (ICICI Lombard)
  21. What are influential and leverage points? Which of them has more effect on the model? (ANZ, Wells Fargo)
  22. Does multicollinearity impact the prediction of a machine learning algorithm? (Wells Fargo)

Logistic regression:

  1. What are the different types of loss functions in regression and classification?
  2. What is the difference between R2 and adjusted R2?
  3. What are the basics assumption of linear regression and logistics regression?
  4. How to check for multicollinearity in datasets and do multicollinearity affects the final performance of model?
  5. In linear regression train R2 is 0.95 & test R2 = 0.93 but |y-y`| is large. How is this possible?
  6. How is hypothesis testing used in linear regression?
  7. How to decide feature importance in linear regression?
  8. How do you decide whether your linear regression model fits the data?
  9. What is the formula of loss function in logistics regression?
  10. Why MSE can’t be used as a loss function for logistics regression?
  11. How to decide feature importance in Logistic regression?
  12. What is the difference between L1 and L2 regularization and why L1 create sparsity?
  13. How to do bias & variance tradeoff? How to find out model is overfitting or not?
  14. What are different types of optimization techniques used to train classical ML algorithms and why do we need SGD over GD?
  15. Can we use tanh function in place of sigmoid in logistics regression?
  16. How to do bivariate analysis between a categorical variable and a continuous variable?

The p-value is a number, calculated from a statistical test, that describes how likely you are to have found a particular set of observations if the null hypothesis were true. P-values are used in hypothesis testing to help decide whether to reject the null hypothesis. P > 0.05 is the probability that the null hypothesis is true. 1 minus the P value is the probability that the alternative hypothesis is true. A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected. A P value greater than 0.05 means that no effect was observed.

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