Popular GitHub Repositories for Extratreesclassifier

This is a Malware Detection ML model made using Random Forest Algorithm

Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning

I developed Machine Learning Software with multiple models that predict and classify AID362 biology lab data. Accuracy values are 99% and above, and F1, Recall and Precision scores are average (average of 3) 78.33%. The purpose of this study is to prove that we can establish an artificial intelligence (machine learning) system in health. With my regression model, you can predict whether it is Inactive or Inactive (Neural Network or Extra Trees). In classification (Neural Network or Extra Trees), you can easily classify the provided data whether it is Inactive or Active.

The objective of this project is to determine the risk of default that a client presents and assign a risk rating to each client. The risk rating will determine if the company will approve (or reject) the loan application

This repo is the Machine Learning practice on NHANES dataset of Heart Disease prediction. The ML algorithms like LR, DT, RF, SVM, KNN, NB, MLP, AdaBoost, XGBoost, CatBoost, LightGBM, ExtraTree, etc. The results are good. I also explore the class-balancing (SMOTE) because the original dataset contains only 5% of patient and 95% of healthy record.

ML models for HR classification problem. For more information please visit the link: https://datahack.analyticsvidhya.com/contest/wns-analytics-hackathon-2018-1/

预测新冠肺炎患者入院早期死亡风险

Predicts the likelihood of kidney stone based on the input parameters.