Python Isolation-forest Resources

Python Decision Tree and Random Forest Decision Tree A Decision Tree is one of the popular and powerful machine learning algorithms that I have learne

🕵️ A machine learning malware analysis framework for Android apps. ☢️ DroidDetective is a Python tool for analysing Android applications (APKs) for p

Kaggle Competition: Forest Cover Type Prediction In this project we predict the forest cover type (the predominant kind of tree cover) using the carto

This machine learning model was developed for House Prices - Advanced Regression Techniques competition in Kaggle by using several machine learning models such as Random Forest, XGBoost and LightGBM.

Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc). Structured a custom ensemble model and a neural network. Found a outperformed model for heart failure prediction accuracy of 88 percent.

A Microsoft Azure Web App project named Covid 19 Predictor using Machine learning Model (Random Forest Classifier Model ) that helps the user to identify whether someone is showing positive Covid symptoms or not by simply inputting certain values like oxygen level , breath rate , age, Vaccination done or not etc. with the help of kaggle database.

Random Forest classifier for neural subtypes extracted from extracellular recordings from human brain organoids.

Driver Analysis with Factors and Forests: An Automated Data Science Tool using Python 📊

You can perform Land Cover Classification on Satellite Images using Random Forest and visualize the result using Earthpy package. Make sure to install the required packages and such as

implementation of machine learning Algorithms such as decision tree and random forest and xgboost on darasets then compare results for each and implement ant colony and genetic algorithms on tsp map, play blackjack game and robot in grid world and evaluate reward for it

Gender-classification This is a ML model to classify Male and Females using some physical characterstics Data. Python Libraries like Pandas,Numpy and

Call of Duty World League: Search & Destroy Outcome Predictions Growing up as an avid Call of Duty player, I was always curious about what factors led

Best-practice-for-network-segmentation What is this? This project was created to publish the best practices for segmentation of the corporate network

Sentiment-Analysis and EDA on the IMDB Movie Review Dataset The main part of the work focuses on the exploration and study of different approaches whi

Python Machine Learning Jupyter Notebooks (ML website) Dr. Tirthajyoti Sarkar, Fremont, California (Please feel free to connect on LinkedIn here) Also

Hyperparameter Optimization of Machine Learning Algorithms This code provides a hyper-parameter optimization implementation for machine learning algor

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities. This is aimed at those looking to get into the field of Data Science or those who are already in the field and looking to solve a real-world project with python.

Patient-Survival - Using Python, I developed a Machine Learning model using classification techniques such as Random Forest and SVM classifiers to predict a patient's survival status that have undergone breast cancer surgery.

House Prices - Advanced Regression Techniques Predicting House Prices with Machine Learning This project is build to enhance my knowledge about machin

House Prices - Advanced Regression Techniques Predicting House Prices with Machine Learning This project is build to enhance my knowledge about machin

The following Python scripts aim to use a Random Forest machine learning algorithm to predict the water affinity of Metal-Organic Frameworks (MOFs). The training set is extracted from the Cambridge Structural Database and the CoRE_MOF 2019 dataset.

This is a GUI interface which can process forest fire detection, smoke detection and fire segmentation. Yolov5 is used to detect fire and smoke and unet is used to segment fire.

Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation (ACM MM 2020) Official implementation of: Forest R-CNN: Large-Vo

Why, hello there! This is the supporting notebook for the research paper — Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomal

The third home of the bare Programming Language (1st there's my heart, the forest came second and then there's Github :)

miceforest: Fast, Memory Efficient Imputation with lightgbm Fast, memory efficient Multiple Imputation by Chained Equations (MICE) with lightgbm. The

TinyWeaklyIsolationForest This repository stores the code to reproduce the results published in "TiWS-iForest: Isolation Forest in Weakly Supervised a

forest-confidence-interval: Confidence intervals for Forest algorithms Forest algorithms are powerful ensemble methods for classification and regressi

Scikit-Garden or skgarden (pronounced as skarden) is a garden for Scikit-Learn compatible decision trees and forests.

generalized random forests A pluggable package for forest-based statistical estimation and inference. GRF currently provides non-parametric methods fo

Feature importances for scikit-learn machine learning models By Terence Parr and Kerem Turgutlu. See Explained.ai for more stuff. The scikit-learn Ran

InfiniteBoost Code for a paper InfiniteBoost: building infinite ensembles with gradient descent (arXiv:1706.01109). A. Rogozhnikov, T. Likhomanenko De

Mixed Effects Random Forest This repository contains a pure Python implementation of a mixed effects random forest (MERF) algorithm. It can be used, o

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

lacmus The program for searching through photos from the air of lost people in the forest using Retina Net neural nwtwork. The project is being develo

datasketch: Big Data Looks Small datasketch gives you probabilistic data structures that can process and search very large amount of data super fast,

Predicting Yelp Review Quality Table of Contents Introduction Motivation Goal and Central Questions The Data Data Storage and ETL EDA Data Pipeline Da

Car Price Prediction Image Credit: AA Cars Project Overview Scraped 3000 used cars data from AA Cars website using Python and BeautifulSoup. Cleaned t

mev-inspect-py Maximal extractable value inspector for Ethereum, to illuminate the dark forest 🌲 💡 Given a block, mev-inspect finds: miner payments

CID3 This is a full-featured Decision Trees and Random Forests learner. It can save trees or forests to disk for later use. It is possible to query tr

Interactive Web App with Streamlit and Scikit-learn that applies different Classification algorithms to popular datasets Datasets Used: Iris dataset,

Table of contents Extended Isolation Forest Summary Motivation Isolation Forest Extension The Code Installation Requirements Use Citation Releases Ext

onelearn: Online learning in Python Documentation | Reproduce experiments | onelearn stands for ONE-shot LEARNning. It is a small python package for o

sklearn-compatible Random Bits Forest Scikit-learn compatible wrapper of the Random Bits Forest program written by Wang et al., 2016, available as a b

IsoTree Fast and multi-threaded implementation of Extended Isolation Forest, Fair-Cut Forest, SCiForest (a.k.a. Split-Criterion iForest), and regular

Python sandbox runners for executing code in isolation aka snekbox.

Amazon Forest Computer Vision Satellite Image tagging code using PyTorch / Keras Here is a sample of images we had to work with Source: https://www.ka

Amazon Forest Computer Vision Satellite Image tagging code using PyTorch / Keras Here is a sample of images we had to work with Source: https://www.ka

TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. The library is a collection of Keras models and supports classification, regression and ranking. TF-DF is a TensorFlow wrapper around the Yggdrasil Decision Forests C++ libraries. Models trained with TF-DF are compatible with Yggdrasil Decision Forests' models, and vice versa.

Forecasting directional movements of stock-prices for intraday trading using LSTM and random-forest https://arxiv.org/abs/2004.10178 Pushpendu Ghosh,

In Loan-Marketing business employees are required to call the user's to buy loans of several fields and in several magnitudes. If employees are calling everybody in the network it is also very lengthy and uncertain that most of the customers will buy it. So, there is a very need for a filtering system that segregates the customers who are unlikely to buy loans and the opposite. Loan-Web is visualized and made up on that context.

The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. I

TreeInterpreter Package for interpreting scikit-learn's decision tree and random forest predictions. Allows decomposing each prediction into bias and

Documentation | External Resources | Research Paper Shapley is a Python library for evaluating binary classifiers in a machine learning ensemble. The

Documentations | Installation | Parameters | Python (scikit-learn) interface What's new? ThunderGBM won 2019 Best Paper Award from IEEE Transactions o

Regularized Greedy Forest Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in this paper. RGF can deliver better r

rpforest rpforest is a Python library for approximate nearest neighbours search: finding points in a high-dimensional space that are close to a given

Master status: Development status: Package information: TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assista

Little Ball of Fur is a graph sampling extension library for Python. Please look at the Documentation, relevant Paper, Promo video and External Resour

Orange Data Mining Orange is a data mining and visualization toolbox for novice and expert alike. To explore data with Orange, one requires no program

Documentations | Installation | Parameters | Python (scikit-learn) interface What's new? ThunderGBM won 2019 Best Paper Award from IEEE Transactions o

Deep Forest (DF) 21 DF21 is an implementation of Deep Forest 2021.2.1. It is designed to have the following advantages: Powerful: Better accuracy than

OPFython: A Python-Inspired Optimum-Path Forest Classifier Welcome to OPFython. Note that this implementation relies purely on the standard LibOPF. Th

MLJAR Automated Machine Learning Documentation: https://supervised.mljar.com/ Source Code: https://github.com/mljar/mljar-supervised Table of Contents

Regularized Greedy Forest Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in this paper. RGF can deliver better r

Master status: Development status: Package information: TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assista

Little Ball of Fur is a graph sampling extension library for Python. Please look at the Documentation, relevant Paper, Promo video and External Resour

Documentation | External Resources | Research Paper Shapley is a Python library for evaluating binary classifiers in a machine learning ensemble. The

H2O H2O is an in-memory platform for distributed, scalable machine learning. H2O uses familiar interfaces like R, Python, Scala, Java, JSON and the Fl