Questions & Answers

Instructor: Applied AI Course Duration: 30 mins

Revision Questions:

  1. Explain about Logistic regression?(
  2. What is Sigmoid function & Squashing ?(
  3. Explain about Optimization problem in logistic regression. (
  4. Importance of Weight vector in logistic regression.(
  5. L2 Regularization: Overfitting and Underfitting.(
  6. L1 regularization and sparsity. (
  7. What is Probabilistic Interpretation: Gaussian Naive Bayes ?(
  8. Explain about Hyperparameter search: Grid Search and Random Search ?(
  9. What is Column Standardization.?(
  10. Explain about Collinearity of features?(
  11. Find Train & Run time space and time complexity of Logistic regression?(

Self Learning:

  1. After analysing 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?(
  2. What are the basic assumptions to be made for linear regression?(Refer:
  3. What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?(
  4. When would you use GD over SDG, and vice-versa?(
  5. How do you decide whether your linear regression model fits the data?(
  6. Is it possible to perform logistic regression with Microsoft Excel?(
  7. When will you use classification over regression?(
  8. Why isn't Logistic Regression called Logistic Classification?(Refer :

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