105 Open source projects that are alternatives of or similar to Cnn Svm Classifier

Master's degree project: Development of a trading algorithm which uses supervised machine learning classification techniques to generate buy/sell signals

도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.

simple tutorial on Machine Learning with Scikitlearn

This repo contains regression and classification projects. Examples: development of predictive models for comments on social media websites; building classifiers to predict outcomes in sports competitions; churn analysis; prediction of clicks on online ads; analysis of the opioids crisis and an analysis of retail store expansion strategies using…

A super-fast and scalable Random Forest library based on fast histogram decision tree algorithm and distributed bagging framework. It can be used for binary classification, multi-label classification, and regression tasks. This library provides both Python and command line interface to users.

Vídeos e códigos do Universo Discreto ensinando o fundamental de Machine Learning em Python. Para mais detalhes, acompanhar a playlist listada.

Jupyter Notebooks and miscellaneous

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

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Machine Learning University: Decision Trees and Ensemble Methods

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Machine Learning Lectures at the European Space Agency (ESA) in 2018

Solution of the Titanic Kaggle competition

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Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand

A python library to build Model Trees with Linear Models at the leaves.

Random Forests for Density Estimation in Python

gesture recognition toolkit

Using LSTM network for time series forecasting

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利用pytorch实现图像分类的一个完整的代码，训练，预测，TTA，模型融合，模型部署，cnn提取特征，svm或者随机森林等进行分类，模型蒸馏，一个完整的代码

Machine Learning algorithms coded from scratch

A Unified Framework for Random Forest Prediction Error Estimation

A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.

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

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

(BOW, TF-IDF, Word2Vec, BERT) Word Embeddings + (SVM, Naive Bayes, Decision Tree, Random Forest) Base Classifiers + Pre-trained BERT on Tensorflow Hub + 1-D CNN and Bi-Directional LSTM on IMDB Movie Reviews Dataset

ThunderGBM: Fast GBDTs and Random Forests on GPUs

Online machine learning methods

Python code for common Machine Learning Algorithms

Predicting the winner of 2019 cricket world cup using random forest algorithm

文本分类基准测试

📈 Click-Through Rate Prediction using Logistic Regression and Tree Algorithms

Classifies genes as an oncogene, tumor suppressor gene, or as a non-driver gene by using Random Forests

Efficient Online Transfer Learning for 3D Object Detection in Autonomous Driving

Generalized Random Forests

A Random Survival Forest implementation for python inspired by Ishwaran et al. - Easily understandable, adaptable and extendable.

Generative Forests in Python

bitcoin prediction algorithms

A curated list of data mining papers about fraud detection.

Ruby Scoring API for PMML

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A tool for visualizing the structure and performance of Random Forests 🌳

Machine Learning library for the web and Node.

A document covering machine learning basics. 🤖📊

This repo is intended to support replication and exploration of the analysis undertaken for our Urban Studies article "Understanding urban gentrification through Machine Learning: Predicting neighbourhood change in London".

It's a github repo star predictor that tries to predict the stars of any github repository having greater than 100 stars.

A curated list of gradient boosting research papers with implementations.

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18

Receipt.ID is a multi-label, multi-class, hierarchical classification system implemented in a two layer feed forward network.

useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html

Machine learning algorithm such as KNN,Naive Bayes,Logistic Regression,SVM,Decision Trees,Random Forest,k means and Truncated SVD on amazon fine food review

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H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Machine learning for C# .Net

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.