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Regression

Q. What is Logistic Regression?
Q. What is Tweedie Regression?
Q. What is Beta regression?
Q. What is Gamma Regression?
Q. Briefly discuss other models that fall within the scope of GLM.
Q. What about cases where a significant number of observations have a count of 0 (in the context of Poisson Regression)?
Q. What is overdispersion in Poisson Regression, and what are alternate specifications for when it is present?
Q. What is the cost function used in Poisson Regression?
Q. How does GLM adjust to the case of count data?
Q. What is the error / loss function in logistic regression?
Q. What are the advantages and disadvantages of logistic regression?
Q. What is the equivalent of the overall F test in logistic regression?
Q. Why are coefficients estimated through Maximum Likelihood (MLE) instead of Least Squares?
Q. How are the coefficients in a logistic expression interpreted?
Q. What is the relationship between the log odds ratio and probability?
Q. Why are the log odds used in the link function instead of just the regular odds ratio?
Q. What problems would arise from using a regular linear regression to model a binary outcome?
Q. What are the assumptions of logistic regression?
Q. What is a Generalized Linear Model (GLM)?
Q. What is non-negative least squares, and when is it used?
Q. What are potential problems encountered in Linear Regression?
Q. What is a high influence point?
Q. What is a high leverage point?
Q. What is an outlier?
Q. What is the difference between outliers, high leverage points, and high influence points?
Q. What is the difference between Regression and ANOVA?
Q. Why does multicollinearity result in poor estimates of coefficients in linear regression?
Q. Doesn’t polynomial regression violate the multicollinearity assumption for Linear Regression?
Q. What are some approaches for modeling non linear relationships?
Q. Differentiate between Linear Models and Non Linear Models
Q. What are the most common transformations when the target variable is not normally distributed?
Q. How can categorical predictors be incorporated in linear regression?
Q. Suppose there are a large number of predictors ‘p’. What is the best approach to find out if any of the p predictors are helpful in predicting the response ‘y’?
Q. What are some of the problems with stepwise selection approaches?
Q. What is Information Criteria (AIC, BIC)?
Q. What are the various measures of error (MSE, RMSE, MAE)?
Q. What is R-squared and adjusted R-squared?
Q. What is Global F-Test?
Q. What are the evaluation criteria for a Linear Regression model?
Q. What is multicollinearity and how can that be identified?
Q. How is variability measured in Linear Regression?
Q. How are coefficients of linear regression estimated?
Q. What are some methods of Variable Selection?
Q. What are the assumptions of linear regression?
Q. What does Gradient in Gradient Boosted Trees refer to?
Q. What is XGBoost? How does it improve upon standard GBM?
Q. What is the difference between Adaboost and Gradient boost?
Q. Distinguish between a Weak learner and a Strong Learner
Q. How is Gradient Boosting different from Random Forest?
Q. What are the advantages and disadvantages of a GBM model?
Q. What are the key hyperparameters for a GBM model?
Q. What is Gradient Boosting (GBM)? Describe how does the Gradient Boosting algorithm work
Q. What is the difference between Decision Trees, Bagging and Random Forest?
Q. Why is Random Forest a non-linear model? Why does it result in non-linear decision boundaries?
Q. What are the advantages and disadvantages of Random Forest?
Q. What are the key hyperparameters for a Random Forest model?
Q. Explain the concept and working of the Random Forest model
Q. What is CART?
Q. Explain the concept of Linear Regression
Q. How does a decision tree create splits from continuous features?
Q. Explain the difference between Entropy, Gini, and Information Gain
Q. Regression vs. Classification

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Explore Questions by Topics

Computer Vision (15)
Generative AI (4)
Reinforcement Learning (13)
Machine Learning Basics (18)
Deep Learning (78)
- DL Basics (16)
- DL Architectures (21)
  - Feedforward Network / MLP (3)
  - Sequence models (6)
  - Transformers (11)
- DL Training and Optimization (39)
Natural Language Processing (34)
- NLP Data Preparation (18)
Supervised Learning (115)
- Regression (41)
  - Linear Regression (26)
  - Generalized Linear Models (9)
  - Regularization (6)
- Classification (70)
  - Logistic Regression (10)
  - Support Vector Machine (9)
  - Ensemble Learning (24)
  - Other Classification Models (9)
  - Classification Evaluations (9)
Unsupervised Learning (64)
- Clustering (40)
  - Distance Measures (9)
  - K-Means Clustering (10)
  - Hierarchical Clustering (3)
  - Gaussian Mixture Models (5)
  - Clustering Evaluations (5)
- Dimensionality Reduction (12)
Statistics (35)
Data Preparation (35)
- Feature Engineering (30)
- Sampling Techniques (5)

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Other Questions in Regression

What is a Gaussian Mixture Model (GMM)?
What is Supervised Fine-Tuning?
What is the problem with using a generic list of stop words?
What is Underfitting?
T5 Architecture Explained & Encoder-Decoder Model Comparison
Rewards in Reinforcement Learning: What They Are & How to Design

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