Lecture
Specification Testing and Machine Learning
Description
This lecture covers specification testing in classical statistics and machine learning, emphasizing the importance of model evaluation through statistical tests and prediction capability. It discusses the strategy for specification testing, informal tests, statistical hypothesis tests, and goodness of fit. The instructor explains the concept of overfitting in machine learning models and introduces regularization techniques to prevent it. The lecture also delves into prediction tests, market segmentation analysis, and the use of nonlinear specifications. Practical examples and methods for model assessment, selection, and variable selection are presented, along with the concept of bias/variance trade-off in model performance.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (59)
Model Selection Criteria: AIC, BIC, Cp
Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.
Machine Learning Fundamentals: Regularization and Cross-validation
Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.
Document Analysis: Topic Modeling
Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Regularization in Machine Learning
Explores Ridge and Lasso Regression for regularization in machine learning models, emphasizing hyperparameter tuning and visualization of parameter coefficients.
Machine learning: Basics of data-driven materials modeling
Covers dimensionality reduction and linear regression in data-driven materials modeling.