Lecture

Logistic Regression: Interpretation & Feature Engineering

Related lectures (107)
Convex Functions
Covers the properties and operations of convex functions.
Probabilistic Linear Regression
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Support Vector Machine and Logistic Regression
Explains support vector machine and logistic regression for classification tasks, emphasizing margin maximization and risk minimization.
Linear and Logistic Regression
Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.
Decision Trees: Regression and Classification
Covers decision trees for regression and classification, explaining tree construction, feature selection, and criteria for induction.
Linear Discriminant Analysis: Generative Methods
Covers Linear Discriminant Analysis (LDA) as a generative method for classification.
Statistical Inference and Machine Learning
Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.
Kernel Methods: Nonlinear Separation Surfaces
Explores kernel methods for nonlinear separation surfaces using polynomial and Gaussian kernels in Perceptron and SVM algorithms.
Applied Machine Learning
Introduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.
LASSO Regression: Sparse Signal Induction
Explores LASSO regression for inducing sparsity in signals through gradient descent.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.