Regression Again: Exercise 3.1

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Description

This lecture covers exercises related to regression, including linear regression, high dimensions, real data analysis on Boston house prices, and polynomial regression. The exercises involve generating data, evaluating models, and exploring regularization techniques.

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Statistics

Data analysis: Regression analysis

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In course

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Instructor

consequat consectetur velit nostrud

Ontological neighbourhood

Statistics

Data analysis: Regression analysis

Related lectures (30)

Regression: High Dimensions

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Linear Regression and Logistic Regression

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

Overfitting in Supervised Learning: Case Studies and Techniques

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Polynomial Regression: Basics and Regularization

Covers the basics of polynomial regression and regularization to prevent overfitting.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.