Feature-engine: A Python library for Feature Engineering for Machine Learning¶
Feature-engine is a Python library with multiple transformers to engineer features for
use in machine learning models. Feature-engine preserves Scikit-learn functionality with
transform() to learn parameters from and then transform the data.
Feature-engine includes transformers for:
Missing data imputation
Categorical variable encoding
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. That is, one object with the entire sequence of variable transformations to leave the raw data ready to be consumed by a machine learning algorithm, and the machine learning model at the back. Check the quickstart for an example.
Would you like to know more about what is unique about Feature-engine?
This article provides a nice summary:
Feature-engine is a Python 3 package and works well with 3.6 or later. Earlier versions have not been tested. 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.
Ingeniería de variables para machine learning, Curso Online.
Ingeniería de variables, MachinLenin, charla online.
More resources in the Learning Resources sections on the navigation panel on the left.
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 value
EndTailImputer: replaces missing data in numerical variables by numbers at the distribution tails
CategoricalImputer: replaces missing data in categorical variables with the string ‘Missing’ or by the most frequent category
RandomSampleImputer: replaces missing data with random samples of the variable
AddMissingIndicator: adds a binary missing indicator to flag observations with missing data
DropMissingData: removes rows containing NA values from dataframe
Categorical Variable Encoders: Encoders¶
OneHotEncoder: performs one hot encoding, optional: of popular categories
CountFrequencyEncoder: replaces categories by 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
Numerical Variable Transformation: Transformers¶
LogTransformer: performs logarithmic transformation of numerical variables
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
Variable Discretisation: Discretisers¶
ArbitraryDiscretiser: sorts variable into intervals arbitrarily defined by the user
EqualFrequencyDiscretiser: sorts variable into equal frequency intervals
EqualWidthDiscretiser: sorts variable into equal size contiguous intervals
DecisionTreeDiscretiser: uses decision trees to create finite variables
Outlier Capping or Removal¶
SklearnTransformerWrapper: executes Scikit-learn various transformers only on the selected subset of features
DropFeatures: drops a 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 feature from correlated group
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 docs (you’re here!).
Stack Overflow. If you ask a question, please tag it with “feature-engine”.
If you are enrolled in the Feature Engineering for Machine Learning course in Udemy , post a question in a relevant section.
Join our mailing list.
Ask a question in the repo by filing an issue.
Found a Bug or have a suggestion?¶
Interested in contributing to Feature-engine? That is great news!
Feature-engine is a welcoming and inclusive project and it would be great 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 “Contributing” link on the left of this page.
Feature-engine’s license is an open source BSD 3-Clause.