Lecture
Logistic Regression: Probability Modeling
Related lectures (34)
Logistic Regression: Probability Modeling and Optimization
Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.
Logistic Regression: Probabilistic Interpretation
Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.
Multiclass Classification
Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.
Probabilistic Linear Regression
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Generalized Linear Regression: Classification
Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.
Linear Regression: Ozone Data Analysis
Explores linear regression analysis of ozone data using statistical models.
Linear Regression: Estimation and Testing
Explores linear regression estimation, hypothesis testing, and practical applications in statistics.
Red bus/Blue bus paradox
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Estimation and Confidence Intervals
Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.
Logistic Regression: Statistical Inference and Machine Learning
Covers logistic regression, likelihood function, Newton's method, and classification error estimation.