⚡️⚡️⚡️《机器学习实战》代码(基于Python3)🚀
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Updated
Feb 5, 2020 - Python
⚡️⚡️⚡️《机器学习实战》代码(基于Python3)🚀
Building Decision Trees From Scratch In Python
Time Series Decomposition techniques and random forest algorithm on sales data
MF-BAVART model introduced in "Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs"
Machine Learning algorithms coded from scratch
Official repository of RankEval: An Evaluation and Analysis Framework for Learning-to-Rank Solutions.
Tree based algorithm in machine learning including both theory and codes. Topics including from decision tree regression and classification to random forest tree and classification. Grid Search is also included.
An easy-to-use scikit-learn inspired implementation of the Standard Genetic Programming (StdGP) algorithm.
Python package for Bayesian Model Mixing
Decision Tree to predict the value of a continuous target variable
A real life case study of property price prediction based on data of New York City.
Regression trees for interval censored output 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
Use regression tree to predict firearm death rate with firearm law & CDC firearm death rate data.
《机器学习实战》代码和数据。The code and data of Machine Learning in Action.
This repository is a collection of both basic and advanced code templates for Model Building. All codes I am sharing are from the practical exercises I did from the Data Science Infinity Program.
Dtreehub is a lightweight decision tree framework for Python with categorical feature support. It covers regular decision tree algorithms: ID3, C4.5, CART, CHAID and regression tree, random forest and adaboost.
Various techniques applied for the prediction of median home value were- Generalized Linear Regression, Regression Tree, Generalized Additive Model and Neural Networks.
Implementation of CART and Random Forest in C++
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