Concept

Ordered logit

Related lectures (32)

Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.

Red bus/Blue bus paradox

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Maximum Likelihood Estimation: Theory

Covers the theory behind Maximum Likelihood Estimation, discussing properties and applications in binary choice and ordered multiresponse models.

Logistic Regression

Covers logistic regression for linear classification and unsupervised dimensionality reduction techniques.

Logistic Regression: Probabilistic Interpretation

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

Linear and Logistic Regression

Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.

Convex Optimization: Examples of Convex Functions

Explores convex optimization, convex functions, and their properties, including strict convexity and strong convexity, as well as different types of convex functions like linear affine functions and norms.

Mixture models: alternative specific variance

Explores alternative specific variance in mixture models and discusses identification issues and model comparisons using 500 draws.

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Logistic Regression: Fundamentals and Applications

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Binary Response: Link Functions

Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.

Logistic Regression: Probability Mapping

Explains logistic regression, mapping predictions to probabilities and optimizing likelihood.

Generalized Linear Models: Examples and Applications

Explores special examples of Generalized Linear Models, covering logistic regression, count data models, separation issues, and nonparametric relationships.

Linear Models for Classification

Covers linear models for classification, logistic regression training, evaluation metrics, and decision boundaries.

Supervised Learning Essentials

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Modern NLP: A Simple Neural Classifier

Introduces the basics of modern NLP, covering word embeddings, neural models, and tasks like sequence labeling and text generation.

Logistic Regression and Polynomial Regression

Introduces logistic regression for binary classification and polynomial regression on polynomial variables.

Logistic Regression: Statistical Inference and Machine Learning

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.