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recipes (version 0.1.17)

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

218,906

Version

0.1.17

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

September 27th, 2021

Functions in recipes (0.1.17)

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

Extract transformed training set

Quantitatively check on variables

Check to see if a recipe is trained/prepared

Apply a trained preprocessing recipe

get_keep_original_cols

Get the keep_original_cols value of a recipe step

Helper Functions for Profile Data Sets

Discretize Numeric Variables

Wrapper function for preparing recipes within resampling

Helpers for printing step functions

Create a Formula from a Prepared Recipe

required_pkgs.step_depth

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

Objects exported from other packages

step_YeoJohnson

Yeo-Johnson Transformation

Sort rows using dplyr

Box-Cox Transformation for Non-Negative Data

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

step_bin2factor

Create a Factors from A Dummy Variable

B-Spline Basis Functions

check that newly created variable names don't overlap

Create a recipe for preprocessing data

Check for Missing Values

Make a random identification field for steps

Cut a numeric variable into a factor

Date Feature Generator

recipes_pkg_check

Update packages

Estimate a preprocessing recipe

recipes_eval_select

Evaluate a selection with tidyselect semantics specific to recipes

High Correlation Filter

Methods for Selecting Variables in Step Functions

Manually Alter Roles

step_downsample

Down-Sample a Data Set Based on a Factor Variable

step_impute_knn

Impute via k-nearest neighbors

step_impute_bag

Impute via bagged trees

step_impute_linear

Impute numeric variables via a linear model

step_hyperbolic

Hyperbolic Transformations

Dummy Variables Creation

ICA Signal Extraction

step_dummy_multi_choice

Multiple Choice Dummy Variables Creation

Create Counts of Patterns using Regular Expressions

step_factor2string

Convert Factors to Strings

Distance between two locations

Filter rows using dplyr

step_impute_mode

Impute nominal data using the most common value

Internal Functions

step_indicate_na

Create Missing Data Column Indicators

Convert values to predefined integers

step_impute_lower

Impute numeric data below the threshold of measurement

step_impute_roll

Impute numeric data using a rolling window statistic

Add intercept (or constant) column

Create Interaction Variables

recipes: A package for computing and preprocessing design matrices.

Holiday Feature Generator

step_discretize

Discretize Numeric Variables

Distances to Class Centroids

Add sin and cos terms for harmonic analysis

Centering numeric data

Polynomial Kernel PCA Signal Extraction

Kernel PCA Signal Extraction

Isomap Embedding

step_impute_mean

Impute numeric data using the mean

Add new variables using dplyr

Mutate multiple columns using dplyr

step_impute_median

Impute numeric data using the median

NNMF Signal Extraction

Simple Value Assignments for Novel Factor Levels

Remove observations with missing values

Center and scale numeric data

Inverse Logit Transformation

Inverse Transformation

Logarithmic Transformation

Logit Transformation

Near-Zero Variance Filter

step_ordinalscore

Convert Ordinal Factors to Numeric Scores

Create a lagged predictor

Collapse Some Categorical Levels

Linear Combination Filter

Radial Basis Function Kernel PCA Signal Extraction

Rename multiple columns using dplyr

Rename variables by name using dplyr

Natural Spline Basis Functions

Create a Profiling Version of a Data Set

Scaling Numeric Data to a Specific Range

PCA Signal Extraction

Relevel factors to a desired level

Apply (Smoothed) Rectified Linear Transformation

Shuffle Variables

Ratio Variable Creation

step_string2factor

Convert Strings to Factors

Create Dummy Variables using Regular Expressions

General Variable Filter

Assign missing categories to "unknown"

Update a recipe step

Sample rows using dplyr

Filter rows by position using dplyr

Moving Window Functions

Zero Variance Filter

Summarize a recipe

Select Terms in a Step Function.

tunable.step_bs

Find recommended methods for generating parameter values

step_spatialsign

Spatial Sign Preprocessing

tidy.step_BoxCox

Tidy the Result of a Recipe

Square Root Transformation

Convert Ordered Factors to Unordered Factors

Up-Sample a Data Set Based on a Factor Variable

Partial Least Squares Feature Extraction

Orthogonal Polynomial Basis Functions

step_num2factor

Convert Numbers to Factors

Scaling Numeric Data

Select variables using dplyr

Add a New Operation to the Current Recipe

Check Variable Class

Check if all Columns are Present

check_new_values

Check for New Values

Check Range Consistency