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fast_rnrr

Source code for the paper "Quasi-Newton Solver for Robust Non-Rigid Registration" (CVPR2020 Oral).

fast_tabnet

TabNet for fastai

fattail

Scalable semiparametrics for heavy tails

fbmq

Facebook Messenger Platform Python Library (Facebook Chatbot Library)

fbpredictivevar

FB predictive var feature selection

fbprophet-price-prediction

Jupyter notebook for performing price predictions of stock data using Facebook's Prophet package.

fbranks

Association Football (Soccer) Ranking via Poisson Regression

fca_hypernymy

fcbf

Houses implementation of the Fast Correlation-Based Filter (FCBF) feature selection method.

fcnnls

Fast Combinatorial Non-negative Least Squares

fcube

FCUBE: a platform for collaborative learning

fdrbasednb

FDR-based categorical variables selection in Naive Bayes classification

fealect

Scores features for Feature seLection

feasts

Feature Extraction And Statistics for Time Series

featkit

Feature engineering toolkit, designed to work with scikit-learn.

feature-engineering-book

Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018

feature-engineering-live-sessions

feature-engineering-made-easy

Feature Engineering Made Easy, published by Packt

feature-hashing-walkthrough

Companion code for http://amunategui.github.io/feature-hashing/

feature-scaling

Python script used for data normalisation in machine learning.

feature-scaling-1

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

feature-selection

Selecting features for classification with MRMR

feature-selection-1

testing feature selection techniques

feature-selection-algorithm

feature-selection-for-machine-learning

feature-selection-with-nearest-neighbor

Feature Selection with Nearest Neighbor

featureextraction

Feature extraction

featureforge

A set of tools for creating and testing machine learning features, with a scikit-learn compatible API

featurefu

Library and tools for advanced feature engineering

features

This is a collection of feature extraction/selection algorithms