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Source code for the paper "Quasi-Newton Solver for Robust Non-Rigid Registration" (CVPR2020 Oral).
TabNet for fastai
Scalable semiparametrics for heavy tails
Facebook Messenger Platform Python Library (Facebook Chatbot Library)
FB predictive var feature selection
Jupyter notebook for performing price predictions of stock data using Facebook's Prophet package.
Association Football (Soccer) Ranking via Poisson Regression
Houses implementation of the Fast Correlation-Based Filter (FCBF) feature selection method.
Fast Combinatorial Non-negative Least Squares
FCUBE: a platform for collaborative learning
FDR-based categorical variables selection in Naive Bayes classification
Scores features for Feature seLection
Feature Extraction And Statistics for Time Series
Feature engineering toolkit, designed to work with scikit-learn.
Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018
Feature Engineering Made Easy, published by Packt
Companion code for http://amunategui.github.io/feature-hashing/
Python script used for data normalisation in machine learning.
Feature magnitude matters because: The regression coefficients of linear models are directly influenced by the scale of the variable. Variables with bigger magnitude / larger value range dominate over those with smaller magnitude / value range Gradient descent converges faster when features are on similar scales Feature scaling helps decrease the time to find support vectors for SVMs Euclidean distances are sensitive to feature magnitude. Some algorithms, like PCA require the features to be centered at 0. The machine learning models affected by the feature scale are: Linear and Logistic Regression Neural Networks Support Vector Machines KNN K-means clustering Linear Discriminant Analysis (LDA) Principal Component Analysis (PCA) Feature Scaling Feature scaling refers to the methods or techniques used to normalize the range of independent variables in our data, or in other words, the methods to set the feature value range within a similar scale. Feature scaling is generally the last step in the data preprocessing pipeline, performed just before training the machine learning algorithms. There are several Feature Scaling techniques, which we will discuss throughout this section: Standardisation Mean normalisation Scaling to minimum and maximum values - MinMaxScaling Scaling to maximum value - MaxAbsScaling Scaling to quantiles and median - RobustScaling Normalization to vector unit length
Selecting features for classification with MRMR
testing feature selection techniques
Feature Selection with Nearest Neighbor
Feature extraction
A set of tools for creating and testing machine learning features, with a scikit-learn compatible API
Library and tools for advanced feature engineering
This is a collection of feature extraction/selection algorithms
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.