⚡️⚡️⚡️《机器学习实战》代码（基于Python3）🚀

Official repository of RankEval: An Evaluation and Analysis Framework for Learning-to-Rank Solutions.

Building Decision Trees From Scratch In Python

Time Series Decomposition techniques and random forest algorithm on sales data

Machine Learning algorithms coded from scratch

Decision Tree to predict the value of a continuous target variable

Generic decision trees for rust

Regression trees for interval censored output data

Use regression tree to predict firearm death rate with firearm law & CDC firearm death rate data.

Moody's Bond Rating Classifier and USPHCI Economic Activity Forecast Modeling

An R package that implements several methods for growing regression trees with functional and multivariate outputs

Various techniques applied for the prediction of median home value were- Generalized Linear Regression, Regression Tree, Generalized Additive Model and Neural Networks.

Boosted cassification and regression trees in python

Exporting trained boosted trees to executable code in plain C / Python

Predicting Baseball Statistics: Regression Application in Python Using scikit-learn

Simple Implementation of Gradient Boosted Trees

This is a presentation I gave to the Econometrics IV class. I described a paper by Patrick Bajari and coauthors. I also described some of the methods the applied in simple terms.

What factors influence runners

Statistical Learning in R

The objective here is to predict the purchase of energy efficient home appliances using various methods like Neural Network, Logistic regression, SVM, Random Forest etc. and then compare the prediction performance in the test data. For estimation, the library sklearn has been used here. This is part of a class project for EconS 514 in Spring 2019 in collaboration with Afrin Islam, Josh Olsen and Jake Wagner.

Assignments, Projects and other course related material.

A real life case study of property price prediction based on data of New York City.

Machine Learning course term project, collaborated with Dr. Daniel Chen, and Dr. Elliot Ash

Using the Consumer Expenditure Microdata to examine how regression trees and linear regression differ in predicting total expenditures.

Hybrid of Regression Trees & Linear Regression

Projects using tree methods (CART, Random Forests, Boosted Trees)

My implementation of the Standard Genetic Programming (STGP) algorithm.

《机器学习实战》代码和数据。The code and data of Machine Learning in Action.

An implementation of 4 machine learning algorithms from scratch