本项目复现李航《统计学习方法》每一章节的算法
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完成情况：
Background This repository contains an implementation of Relaxed Linear Adversarial Concept Erasure (RLACE). Given a dataset X of dense representation
EmeraldML A machine learning library for streamlining the process of (1) cleaning and splitting data, (2) training, optimizing, and testing various mo
imbalanced-learn imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-cla
SLAM-application: installation and test (3D): LeGO-LOAM, LIO-SAM, and LVI-SAM Tested on Quadruped robot in Gazebo ● Results: video, video2 Requirement
Diabetes Prediction Using Decision Tree Introduction Decision tree is the most powerful and popular tool for classification and prediction. A Decision
Implemented four supervised learning Machine Learning algorithms from an algorithmic family called Classification and Regression Trees (CARTs), details see README_Report.
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Domino Research This repo contains projects under active development by the Domino R&D team. We build tools that help Data Scientists and ML engineers
Demand-Forecasting Business Problem A chain of stores, 10 different stores and 50 different requests a 3-month demand forecast for its product.
tsfresh This repository contains the TSFRESH python package. The abbreviation stands for "Time Series Feature extraction based on scalable hypothesis
2021 Machine Learning Security Evasion Competition This repository contains code samples for the 2021 Machine Learning Security Evasion Competition. P
Tangram Website | Discord Tangram makes it easy for programmers to train, deploy, and monitor machine learning models. Run tangram train to train a mo
Machine Learning Videos This is a collection of recorded talks at machine learning conferences, workshops, seminars, summer schools, and miscellaneous
Kaggle Tweet Sentiment Extraction Competition: 1st place solution (Dark of the Moon team)
pytm: A Pythonic framework for threat modeling Introduction Traditional threat modeling too often comes late to the party, or sometimes not at all. In
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