News

Cai Cai's Machine Learning Sklearn Class: Understandable from Scratch, from Model Invocation to Practical Implementation

The design of sklearn follows the "Swiss Army Knife" principle, integrating six core modules: Data Preprocessing: Similar to ...
Visual Studio Magazine

Kernel Ridge Regression with Cholesky Inverse Training Using C#

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single ...
Scientific Research Publishing

Hahn, P.R., Murray, J.S. and Carvalho, C.M. (2020) Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion ...

ABSTRACT: This study investigates the persistent academic impacts of the Head Start program, a federal government-funded early childhood intervention, using data from the Early Childhood Longitudinal ...
GitHub

Summary: This project focuses on supervised learning, particularly linear models, regularization techniques, overfitting, underfitting, and metrics for quality estimation.

In the last project we discussed what ML is and what problems this field of science solves. And also a detailed series of examples of the most difficult tasks and by what group they can be divided.
Hosted on MSN

Regression in Python: How to Find Relationships in Your Data

Simply collecting data is not enough. You can fill spreadsheets with data, but it's useless if you can't act on it. Regression is one of the most powerful statistical tools for finding relationships ...
IEEE

Research on the Enhancement and Improvement of Logistic Regression Models

Abstract: The logistic regression model is a linear model widely used for two-category classification problems. This report examines the enhancement and improvement methods of logistic regression ...