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recipes (version 1.0.0)

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

173,696

Version

1.0.0

License

MIT + file LICENSE

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Repository

https://github.com/tidymodels/recipes

Homepage

https://recipes.tidymodels.org/

Maintainer

Last Published

July 1st, 2022

Functions in recipes (1.0.0)

Check for required column at bake-time

Check if all Columns are Present

check_new_values

Check for New Values

Check for Missing Values

Check Range Consistency

Check Variable Class

Quantitatively check on variables

check that newly created variable names don't overlap

Helper Functions for Profile Data Sets

Extract transformed training set

Helpers for printing step functions

Objects exported from other packages

recipes_pkg_check

Update packages

case-weight-helpers

Helpers for steps with case weights

Using case weights with recipes

get_keep_original_cols

Get the keep_original_cols value of a recipe step

Create a Formula from a Prepared Recipe

Create traditional dummy variables

step_discretize

Discretize Numeric Variables

Add sin and cos terms for harmonic analysis

Holiday Feature Generator

Add a New Operation to the Current Recipe

Create a recipe for preprocessing data

Apply a trained preprocessing recipe

step_impute_median

Impute numeric data using the median

Internal Functions

Methods for selecting variables in step functions

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

Discretize Numeric Variables

B-Spline Basis Functions

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

Estimate a preprocessing recipe

Wrapper function for preparing recipes within resampling

Box-Cox Transformation for Non-Negative Data

Check to see if a recipe is trained/prepared

step_impute_knn

Impute via k-nearest neighbors

step_YeoJohnson

Yeo-Johnson Transformation

Date Feature Generator

step_factor2string

Convert Factors to Strings

step_impute_bag

Impute via bagged trees

Filter rows using dplyr

step_string2factor

Convert Strings to Factors

Centering numeric data

step_impute_mean

Impute numeric data using the mean

Scaling Numeric Data to a Specific Range

High Correlation Filter

Distances to Class Centroids

Make a random identification field for steps

step_impute_lower

Impute numeric data below the threshold of measurement

step_impute_linear

Impute numeric variables via a linear model

Orthogonal Polynomial Basis Functions

Partial Least Squares Feature Extraction

Add new variables using dplyr

step_dummy_extract

Extract patterns from nominal data

step_dummy_multi_choice

Handle levels in multiple predictors together

Rename variables by name using dplyr

Create a lagged predictor

Linear Combination Filter

Update a recipe step

Rename multiple columns using dplyr

Inverse Logit Transformation

Ratio Variable Creation

Inverse Transformation

Create a Profiling Version of a Data Set

step_spatialsign

Spatial Sign Preprocessing

tidy.step_BoxCox

Tidy the Result of a Recipe

Square Root Transformation

Summarize a recipe

step_nnmf_sparse

Non-Negative Matrix Factorization Signal Extraction with lasso Penalization

Center and scale numeric data

Convert Ordered Factors to Unordered Factors

Mutate multiple columns using dplyr

Detect a regular expression

Assign missing categories to "unknown"

Select Terms in a Step Function.

Time Feature Generator

Sort rows using dplyr

recipes: A package for computing and preprocessing design matrices.

Isomap Embedding

recipes_eval_select

Evaluate a selection with tidyselect semantics specific to recipes

Near-Zero Variance Filter

Kernel PCA Signal Extraction

step_hyperbolic

Hyperbolic Transformations

Remove observations with missing values

Non-Negative Matrix Factorization Signal Extraction

required_pkgs.step_depth

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

Manually Alter Roles

Zero Variance Filter

step_percentile

Percentile Transformation

PCA Signal Extraction

step_bin2factor

Create a Factors from A Dummy Variable

Create Counts of Patterns using Regular Expressions

Distance between two locations

step_filter_missing

Missing Value Column Filter

Sample rows using dplyr

Collapse Some Categorical Levels

Apply (Smoothed) Rectified Linear Transformation

Logit Transformation

step_indicate_na

Create Missing Data Column Indicators

Cut a numeric variable into a factor

ICA Signal Extraction

Convert values to predefined integers

step_num2factor

Convert Numbers to Factors

step_impute_roll

Impute numeric data using a rolling window statistic

step_impute_mode

Impute nominal data using the most common value

Relevel factors to a desired level

Add intercept (or constant) column

Create Interaction Variables

Filter rows by position using dplyr

step_ordinalscore

Convert Ordinal Factors to Numeric Scores

Logarithmic Transformation

Simple Value Assignments for Novel Factor Levels

Polynomial Kernel PCA Signal Extraction

Radial Basis Function Kernel PCA Signal Extraction

Scaling Numeric Data

General Variable Filter

Moving Window Functions

Shuffle Variables

update_role_requirements

Update role specific requirements

Select variables using dplyr

Natural Spline Basis Functions