238 Open source projects that are alternatives of or similar to wetlandmapR

Machine Learning inference engine for Microcontrollers and Embedded devices

ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python

Compact Cache V2 is used by ArcGIS to store raster tiles. The bundle file structure is very simple and optimized for quick access, resulting in improved performance over alternative formats.

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python

Reproduce research from paper "Predicting the direction of stock market prices using random forest"

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

A collection of research papers on decision, classification and regression trees with implementations.

Presentations on Quantified Self and Self-Tracking with Python

Text Classification Algorithms: A Survey

Building Decision Trees From Scratch In Python

InfiniteBoost: building infinite ensembles with gradient descent

I will update this repository to learn Machine learning with python with statistics content and materials

Formed trajectories of sets of points.Experimented on finding similarities between trajectories based on DTW (Dynamic Time Warping) and LCSS (Longest Common SubSequence) algorithms.Modeled trajectories as strings based on a Grid representation.Benchmarked KNN, Random Forest, Logistic Regression classification algorithms to classify efficiently t…

🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!

Misc Statistics and Machine Learning codes in R

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

Observable Property and Signal for building data-driven UI without Rx

随机森林，Random Forest（RF）

ML-powered Loan-Marketer Customer Filtering Engine

Machine Learning with the NSL-KDD dataset for Network Intrusion Detection

Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning

This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'

Thin Julia wrapper for GDAL - Geospatial Data Abstraction Library

exploratory data analysis using random forests

🍊 📊 💡 Orange: Interactive data analysis

several methods for text classification

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Heart Disease prediction using 5 algorithms

텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)

My contributions to the #30DayMapChallenge 2019, a daily challenge focusing on spatial visualizations happening throughout November.

A set of tools to understand what is happening inside a Random Forest

Ariyana is an ECS work in progress game engine written in Orthodox C++ and Beef with a focus on cross-platform and multiplayer games

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

A curated list of geospatial tools, data, tutorials, information, and more

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

Simple geographical raster interaction built on top of ArchGDAL, GDAL and CoordinateTransformations

Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

This is the official implementation for the paper 'AutoEncoder by Forest'

YouTube Like Count Predictions using Machine Learning

Conformalized Quantile Regression

Handwritten Digit Recognition using Machine Learning and Deep Learning

Faster, better, smarter ecological niche modeling and species distribution modeling

A New, Interactive Approach to Learning Data Science

Automated database-enabled navigation ✔️ of web applications

Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.

Easy Reduced Basis method

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

Example JavaScript source code for ArcGIS imagery apps (Landsat Explorer and Sentinel Explorer) that you can expand or customize.

This is the implementation of Sparse Projection Oblique Randomer Forest

OptimizeRasters is a set of tools for converting raster data to optimized Tiled TIF or MRF files, moving data to cloud storage, and creating Raster Proxies.

Library for MQL5 (MetaTrader) with Python, Java, Apache Spark, AWS

Small JavaScript implementation of ID3 Decision tree

Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka

A random forest

A fast and easy to use decision tree learner in java

NetCDF support for the julia programming language

In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.

Faster raster processing using GRASS GIS

Machine Learning library written in Python and NumPy.

An end-to-end machine learning and data mining framework on Hadoop