Working on identifying variance and bias
-
Updated
Feb 8, 2017 - MATLAB
Working on identifying variance and bias
I developed a function to perform regularized linear and Gaussian basis functions for regression. Some dataset from the UCI machine learning repository were used to validate the function.
Course work for Machine Learning Course by Stanford University on Coursera
Assessing Beijing’s PM 2.5 pollution: severity, weather impact in R
Andrew Ng's Machine Learning Course
This is the implementation of the five regression methods Least Square (LS), Regularized Least Square (RLS), LASSO, Robust Regression (RR) and Bayesian Regression (BR).
This repository contains several machine learning projects done in Jupyter Notebooks
This repository corresponds to the course "Statistical Learning Theory" taught at the School of Mathematics and Statistics (FME), UPC under the MESIO-UPC-UB Joint Interuniversity Master's Program under the instructor Pedro Delicado
Implementation of Ridge Regression Algorithm (Regularised Linear Regression)
Solutions to Coursera's Intro to Machine Learning course in python
Using Multiple Linear Regression and Lasso Regression (Linear Regression with L1 norm) to predict bike rentals by registered users based on weather and time criteria.
This are my solutions to the course Machine Learning from Coursera by Prof. Andrew Ng
In this project I tried to implement linear regression and regularized linear regression by my own and compare performance to sklearn model.
Regularized regression using a forest fire data set
Housing price prediction using Regularised linear regression
Implementation of Linear Ridge regression and Regularized logistic regression
Sequential adaptive elastic net (SAEN) approach, complex-valued LARS solver for weighted Lasso/elastic-net problems, and sparsity (or model) order detection with an application to single-snapshot source localization.
Add a description, image, and links to the regularized-linear-regression topic page so that developers can more easily learn about it.
To associate your repository with the regularized-linear-regression topic, visit your repo's landing page and select "manage topics."