Lecture
Feature Engineering: Polynomial Regression
Description
This lecture covers the concept of feature engineering, focusing on fitting linear regression on features of the original predictors. It includes topics such as data cleaning, transformations of the input and output, and the use of splines for flexible feature representation.
This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.
Watch on MediaspaceOfficial 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 (291)
Transformations of Input or Output
Covers handling missing data, feature engineering, and output transformations in machine learning.
Logistic Regression: Vegetation Prediction
Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.
Gradient Descent: Linear Regression
Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.
Feature Engineering: Missing Data and Standardization
Covers techniques for handling missing data and standardizing features, as well as transforming input and output data.
Document Analysis: Topic Modeling
Explores document analysis, topic modeling, and generative models for data generation in machine learning.