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recipes (version 0.2.0)

Preprocessing and Feature Engineering Steps for Modeling

Description

A recipe prepares your data for modeling. We provide an extensible framework for pipeable sequences of feature engineering steps provides preprocessing tools to be applied to data. Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting processed output can then be used as inputs for statistical or machine learning models.

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Install

install.packages('recipes')

Monthly Downloads

215,376

Version

0.2.0

License

MIT + file LICENSE

Issues

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Repository

https://github.com/tidymodels/recipes

Homepage

https://recipes.tidymodels.org/

Maintainer

Max Kuhn

Last Published

February 18th, 2022

Functions in recipes (0.2.0)

Check for Missing Values

check_new_values

Check for New Values

Check if all Columns are Present

Apply a trained preprocessing recipe

Add a New Operation to the Current Recipe

check that newly created variable names don't overlap

Check Variable Class

Detect if a particular step or check is used in a recipe

Check Range Consistency

Quantitatively check on variables

Helpers for printing step functions

Helper Functions for Profile Data Sets

Create a recipe for preprocessing data

Discretize Numeric Variables

Make a random identification field for steps

Extract transformed training set

recipes: A package for computing and preprocessing design matrices.

Check to see if a recipe is trained/prepared

Internal Functions

Create a Formula from a Prepared Recipe

get_keep_original_cols

Get the keep_original_cols value of a recipe step

recipes_eval_select

Evaluate a selection with tidyselect semantics specific to recipes

recipes_pkg_check

Update packages

Sort rows using dplyr

step_YeoJohnson

Yeo-Johnson Transformation

step sets the class of the step and check is for checks.

Box-Cox Transformation for Non-Negative Data

Centering numeric data

Objects exported from other packages

required_pkgs.step_depth

S3 methods for tracking which additional packages are needed for steps.

Manually Alter Roles

Estimate a preprocessing recipe

Methods for selecting variables in step functions

Distances to Class Centroids

step_dummy_multi_choice

Handle levels in multiple predictors together

step_dummy_extract

Extract patterns from nominal data

step_discretize

Discretize Numeric Variables

Wrapper function for preparing recipes within resampling

B-Spline Basis Functions

step_downsample

Down-Sample a Data Set Based on a Factor Variable

step_bin2factor

Create a Factors from A Dummy Variable

High Correlation Filter

Create traditional dummy variables

step_factor2string

Convert Factors to Strings

Cut a numeric variable into a factor

step_hyperbolic

Hyperbolic Transformations

Filter rows using dplyr

Distance between two locations

Linear Combination Filter

step_filter_missing

Missing Value Column Filter

Isomap Embedding

step_impute_mean

Impute numeric data using the mean

step_impute_median

Impute numeric data using the median

step_impute_roll

Impute numeric data using a rolling window statistic

step_impute_mode

Impute nominal data using the most common value

step_impute_knn

Impute via k-nearest neighbors

Create a lagged predictor

Remove observations with missing values

Add sin and cos terms for harmonic analysis

Create Counts of Patterns using Regular Expressions

step_impute_bag

Impute via bagged trees

Date Feature Generator

step_indicate_na

Create Missing Data Column Indicators

Logarithmic Transformation

Convert values to predefined integers

Logit Transformation

General Variable Filter

Natural Spline Basis Functions

Simple Value Assignments for Novel Factor Levels

Non-Negative Matrix Factorization Signal Extraction

ICA Signal Extraction

Sample rows using dplyr

Summarize a recipe

Polynomial Kernel PCA Signal Extraction

Radial Basis Function Kernel PCA Signal Extraction

Apply (Smoothed) Rectified Linear Transformation

Add intercept (or constant) column

Holiday Feature Generator

Create Interaction Variables

Inverse Transformation

Select Terms in a Step Function.

tidy.step_BoxCox

Tidy the Result of a Recipe

PCA Signal Extraction

Kernel PCA Signal Extraction

Center and scale numeric data

step_nnmf_sparse

Non-Negative Matrix Factorization Signal Extraction with lasso Penalization

Update a recipe step

step_impute_lower

Impute numeric data below the threshold of measurement

Select variables using dplyr

step_num2factor

Convert Numbers to Factors

Near-Zero Variance Filter

step_impute_linear

Impute numeric variables via a linear model

Partial Least Squares Feature Extraction

Convert Ordered Factors to Unordered Factors

step_percentile

Percentile Transformation

Orthogonal Polynomial Basis Functions

Scaling Numeric Data

Rename variables by name using dplyr

Ratio Variable Creation

Detect a regular expression

Rename multiple columns using dplyr

Inverse Logit Transformation

Assign missing categories to "unknown"

step_spatialsign

Spatial Sign Preprocessing

Square Root Transformation

Up-Sample a Data Set Based on a Factor Variable

step_ordinalscore

Convert Ordinal Factors to Numeric Scores

Shuffle Variables

Collapse Some Categorical Levels

Filter rows by position using dplyr

step_string2factor

Convert Strings to Factors

Moving Window Functions

Scaling Numeric Data to a Specific Range

Mutate multiple columns using dplyr

Create a Profiling Version of a Data Set

Add new variables using dplyr

Relevel factors to a desired level

Zero Variance Filter