A Python library for Feature Engineering and Selection#
Feature-engine is a Python library with multiple transformers to engineer and select
features to use in machine learning models. Feature-engine preserves Scikit-learn
functionality with methods
transform() to learn parameters from and then
transform the data.
Feature-engine includes transformers for:
Missing data imputation
Outlier capping or removal
Feature-engine allows you to select the variables you want to transform within each transformer. This way, different engineering procedures can be easily applied to different feature subsets.
Feature-engine transformers can be assembled within the Scikit-learn pipeline, therefore making it possible to save and deploy one single object (.pkl) with the entire machine learning pipeline. Check **Quick Start** for an example.
What is unique about Feature-engine?#
The following characteristics make Feature-engine unique:
Feature-engine contains the most exhaustive battery of feature engineering transformations.
Feature-engine can transform a specific group of variables in the dataframe.
Feature-engine returns dataframes, hence suitable for data exploration and model deployment.
Feature-engine is compatible with the Scikit-learn pipeline.
Feature-engine automatically recognizes numerical, categorical and datetime variables.
Feature-engine alerts you if a transformation is not possible, e.g., if applying logarithm to negative variables or divisions by 0.
If you want to know more about what makes Feature-engine unique, check this article.
Feature-engine is a Python 3 package and works well with 3.7 or later. Earlier versions are not compatible with the latest versions of Python numerical computing libraries.
The simplest way to install Feature-engine is from PyPI with pip:
$ pip install feature-engine
Note, you can also install it with a _ as follows:
$ pip install feature_engine
Feature-engine is an active project and routinely publishes new releases. To upgrade Feature-engine to the latest version, use pip like this:
$ pip install -U feature-engine
If you’re using Anaconda, you can install the Anaconda Feature-engine package:
$ conda install -c conda-forge feature_engine
Feature-engine features in the following resources#
Feature Engineering for Machine Learning, Online Course.
Feature Selection for Machine Learning, Online Course.
Feature Engineering for Time Series Forecasting, Online Course.
More learning resources in the **Learning Resources**.
Feature-engine hosts the following groups of transformers:
Missing Data Imputation: Imputers#
MeanMedianImputer: replaces missing data in numerical variables by the mean or median
ArbitraryNumberImputer: replaces missing data in numerical variables by an arbitrary number
EndTailImputer: replaces missing data in numerical variables by numbers at the distribution tails
CategoricalImputer: replaces missing data with an arbitrary string or by the most frequent category
RandomSampleImputer: replaces missing data by random sampling observations from the variable
AddMissingIndicator: adds a binary missing indicator to flag observations with missing data
DropMissingData: removes observations (rows) containing missing values from dataframe
Categorical Encoders: Encoders#
OneHotEncoder: performs one hot encoding, optional: of popular categories
CountFrequencyEncoder: replaces categories by the observation count or percentage
OrdinalEncoder: replaces categories by numbers arbitrarily or ordered by target
MeanEncoder: replaces categories by the target mean
WoEEncoder: replaces categories by the weight of evidence
PRatioEncoder: replaces categories by a ratio of probabilities
DecisionTreeEncoder: replaces categories by predictions of a decision tree
RareLabelEncoder: groups infrequent categories
Variable Discretisation: Discretisers#
Outlier Capping or Removal#
Numerical Transformation: Transformers#
LogTransformer: performs logarithmic transformation of numerical variables
LogCpTransformer: performs logarithmic transformation after adding a constant value
ReciprocalTransformer: performs reciprocal transformation of numerical variables
PowerTransformer: performs power transformation of numerical variables
BoxCoxTransformer: performs Box-Cox transformation of numerical variables
YeoJohnsonTransformer: performs Yeo-Johnson transformation of numerical variables
DropFeatures: drops an arbitrary subset of variables from a dataframe
DropConstantFeatures: drops constant and quasi-constant variables from a dataframe
DropDuplicateFeatures: drops duplicated variables from a dataframe
DropCorrelatedFeatures: drops correlated variables from a dataframe
SmartCorrelatedSelection: selects best features from correlated groups
DropHighPSIFeatures: selects features based on the Population Stability Index (PSI)
SelectByShuffling: selects features by evaluating model performance after feature shuffling
SelectBySingleFeaturePerformance: selects features based on their performance on univariate estimators
SelectByTargetMeanPerformance: selects features based on target mean encoding performance
RecursiveFeatureElimination: selects features recursively, by evaluating model performance
RecursiveFeatureAddition: selects features recursively, by evaluating model performance
Can’t get something to work? Here are places where you can find help.
The **User Guide** in the docs.
Stack Overflow. If you ask a question, please mention “feature_engine” in it.
If you are enrolled in the Feature Engineering for Machine Learning course , post a question in a relevant section.
If you are enrolled in the Feature Selection for Machine Learning course , post a question in a relevant section.
Join our gitter community. You an ask questions here as well.
Ask a question in the repo by filing an issue (check before if there is already a similar issue created :) ).
Interested in contributing to Feature-engine? That is great news!
Feature-engine is a welcoming and inclusive project and we would be delighted to have you on board. We follow the Python Software Foundation Code of Conduct.
Regardless of your skill level you can help us. We appreciate bug reports, user testing, feature requests, bug fixes, addition of tests, product enhancements, and documentation improvements. We also appreciate blogs about Feature-engine. If you happen to have one, let us know!
For more details on how to contribute check the contributing page. Click on the **Contribute** guide.
Support Feature-engine financially via Github Sponsors and help further our mission to democratize machine learning tools through open-source software.
Feature-engine’s license is an open source BSD 3-Clause.
Table of Contents#
- Quick Start
- User Guide
- What’s new
- Sponsor us