mesaNeurIPS’20 | Build powerful ensemble class-imbalanced learning models via meta-knowledge-powered resampler. | 设计元知识驱动的采样器解决类别不平衡问题
Stars: ✭ 88 (-55.78%)
ResLTResLT: Residual Learning for Long-tailed Recognition (TPAMI 2022)
Stars: ✭ 40 (-79.9%)
imbalanced-regression[ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
Stars: ✭ 425 (+113.57%)
sklearn-pmml-modelA library to parse and convert PMML models into Scikit-learn estimators.
Stars: ✭ 71 (-64.32%)
kenchiA scikit-learn compatible library for anomaly detection
Stars: ✭ 36 (-81.91%)
skippaSciKIt-learn Pipeline in PAndas
Stars: ✭ 33 (-83.42%)
Sklearn EvaluationMachine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.
Stars: ✭ 294 (+47.74%)
TF-Speech-Recognition-Challenge-SolutionSource code of the model used in Tensorflow Speech Recognition Challenge (https://www.kaggle.com/c/tensorflow-speech-recognition-challenge). The solution ranked in top 5% in private leaderboard.
Stars: ✭ 58 (-70.85%)
subsemblesubsemble R package for ensemble learning on subsets of data
Stars: ✭ 40 (-79.9%)
AdaptiveRandomForestRepository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04
Stars: ✭ 28 (-85.93%)
pycobrapython library implementing ensemble methods for regression, classification and visualisation tools including Voronoi tesselations.
Stars: ✭ 111 (-44.22%)
Ensemble-PytorchA unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.
Stars: ✭ 407 (+104.52%)
sklearn-audio-classificationAn in-depth analysis of audio classification on the RAVDESS dataset. Feature engineering, hyperparameter optimization, model evaluation, and cross-validation with a variety of ML techniques and MLP
Stars: ✭ 31 (-84.42%)
scikit-learnبه فارسی، برای مشارکت scikit-learn
Stars: ✭ 19 (-90.45%)
Orange3🍊 📊 💡 Orange: Interactive data analysis
Stars: ✭ 3,152 (+1483.92%)
handson-ml도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.
Stars: ✭ 285 (+43.22%)
Sklearn PorterTranspile trained scikit-learn estimators to C, Java, JavaScript and others.
Stars: ✭ 1,014 (+409.55%)
Mlatimperial2017Materials for the course of machine learning at Imperial College organized by Yandex SDA
Stars: ✭ 71 (-64.32%)
XcessivA web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
Stars: ✭ 1,255 (+530.65%)
Qlik Py ToolsData Science algorithms for Qlik implemented as a Python Server Side Extension (SSE).
Stars: ✭ 135 (-32.16%)
StackingStacked Generalization (Ensemble Learning)
Stars: ✭ 173 (-13.07%)
Igela delightful machine learning tool that allows you to train, test, and use models without writing code
Stars: ✭ 2,956 (+1385.43%)
Automlpipeline.jlA package that makes it trivial to create and evaluate machine learning pipeline architectures.
Stars: ✭ 223 (+12.06%)
Deep-Vesselkgpml.github.io/deep-vessel/
Stars: ✭ 52 (-73.87%)
Amazing Feature EngineeringFeature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
Stars: ✭ 218 (+9.55%)
bagging puSimple sklearn based python implementation of Positive-Unlabeled (PU) classification using bagging based ensembles
Stars: ✭ 73 (-63.32%)
KMeans elbowCode for determining optimal number of clusters for K-means algorithm using the 'elbow criterion'
Stars: ✭ 35 (-82.41%)
playgroundA Streamlit application to play with machine learning models directly from the browser
Stars: ✭ 48 (-75.88%)
PracticalMachineLearningA collection of ML related stuff including notebooks, codes and a curated list of various useful resources such as books and softwares. Almost everything mentioned here is free (as speech not free food) or open-source.
Stars: ✭ 60 (-69.85%)
Kaio-machine-learning-human-face-detectionMachine Learning project a case study focused on the interaction with digital characters, using a character called "Kaio", which, based on the automatic detection of facial expressions and classification of emotions, interacts with humans by classifying emotions and imitating expressions
Stars: ✭ 18 (-90.95%)
datascienvdatascienv is package that helps you to setup your environment in single line of code with all dependency and it is also include pyforest that provide single line of import all required ml libraries
Stars: ✭ 53 (-73.37%)
AutogluonAutoGluon: AutoML for Text, Image, and Tabular Data
Stars: ✭ 3,920 (+1869.85%)
Profanity CheckA fast, robust Python library to check for offensive language in strings.
Stars: ✭ 354 (+77.89%)
forecastVegA Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python
Stars: ✭ 44 (-77.89%)
Traingenerator🧙 A web app to generate template code for machine learning
Stars: ✭ 948 (+376.38%)
AilearningAiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
Stars: ✭ 32,316 (+16139.2%)
Ml codeA repository for recording the machine learning code
Stars: ✭ 75 (-62.31%)
Dat8General Assembly's 2015 Data Science course in Washington, DC
Stars: ✭ 1,516 (+661.81%)
SkggmScikit-learn compatible estimation of general graphical models
Stars: ✭ 177 (-11.06%)
Hyperparameter hunterEasy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
Stars: ✭ 648 (+225.63%)
multiscorerA module for allowing the use of multiple metric functions in scikit's cross_val_score
Stars: ✭ 21 (-89.45%)
scibloxsciblox - Easier Data Science and Machine Learning
Stars: ✭ 48 (-75.88%)
SktimeA unified framework for machine learning with time series
Stars: ✭ 4,741 (+2282.41%)
RmdlRMDL: Random Multimodel Deep Learning for Classification
Stars: ✭ 375 (+88.44%)
Combo(AAAI' 20) A Python Toolbox for Machine Learning Model Combination
Stars: ✭ 481 (+141.71%)
sl3💪 🤔 Modern Super Learning with Machine Learning Pipelines
Stars: ✭ 93 (-53.27%)
HungabungaHungaBunga: Brute-Force all sklearn models with all parameters using .fit .predict!
Stars: ✭ 614 (+208.54%)
TextClassification基于scikit-learn实现对新浪新闻的文本分类,数据集为100w篇文档,总计10类,测试集与训练集1:1划分。分类算法采用SVM和Bayes,其中Bayes作为baseline。
Stars: ✭ 86 (-56.78%)