Check Range Consistency
Check for New Values
Check for Missing Values
Detect if a particular step or check is used in a recipe
Check if all Columns are Present
check that newly created variable names don't overlap
Quantitatively check on variables
Apply a Trained Data Recipe
Add a New Operation to the Current Recipe
Check Variable Class
Role Selection
Discretize Numeric Variables
Update packages
Wrapper function for preparing recipes within resampling
Extract Finalized Training Set
Print a Recipe
Objects exported from other packages
Get the keep_original_cols value of a recipe step
Check to see if a recipe is trained/prepared
B-Spline Basis Functions
S3 methods for tracking which additional packages are needed for steps.
Manually Alter Roles
Create a Recipe for Preprocessing Data
Naming Tools
Helper Functions for Profile Data Sets
Make a random identification field for steps
Methods for Selecting Variables in Step Functions
Distances to Class Centroids
Train a Data Recipe
High Correlation Filter
Discretize Numeric Variables
step sets the class of the step and check is for checks.
Create a Formula from a Prepared Recipe
Create a Factors from A Dummy Variable
Sort rows using dplyr
Filter rows using dplyr
Date Feature Generator
Centering numeric data
Data Depths
Helpers for printing step functions
Impute Numeric Data Using the Median
Dummy Variables Creation
Impute Nominal Data Using the Most Common Value
ICA Signal Extraction
Convert Factors to Strings
Imputation via Bagged Trees
recipes: A package for computing and preprocessing design matrices.
Internal Functions
Yeo-Johnson Transformation
Impute Numeric Data Using a Rolling Window Statistic
Box-Cox Transformation for Non-Negative Data
Create Missing Data Column Indicators
Imputation via K-Nearest Neighbors
Distance between two locations
Impute Numeric Data Using the Mean
Impute Numeric Data Below the Threshold of Measurement
Kernel PCA Signal Extraction
Imputation of numeric variables via a linear model.
Add new variables using dplyr
Logit Transformation
Inverse Transformation
Add intercept (or constant) column
Down-Sample a Data Set Based on a Factor Variable
NNMF Signal Extraction
Remove observations with missing values
Mutate multiple columns using dplyr
Near-Zero Variance Filter
Convert Numbers to Factors
Center and scale numeric data
Isomap Embedding
Holiday Feature Generator
Hyperbolic Transformations
Create Counts of Patterns using Regular Expressions
Inverse Logit Transformation
Simple Value Assignments for Novel Factor Levels
Cut a numeric variable into a factor
Natural Spline Basis Functions
Radial Basis Function Kernel PCA Signal Extraction
Create a lagged predictor
PCA Signal Extraction
Polynomial Kernel PCA Signal Extraction
Collapse Some Categorical Levels
Convert Ordinal Factors to Numeric Scores
Rename multiple columns using dplyr
Convert values to predefined integers
Apply (Smoothed) Rectified Linear Transformation
Create Interaction Variables
Linear Combination Filter
Scaling Numeric Data to a Specific Range
Ratio Variable Creation
Square Root Transformation
Select variables using dplyr
Rename variables by name using dplyr
Logarithmic Transformation
General Variable Filter
Up-Sample a Data Set Based on a Factor Variable
Zero Variance Filter
Partial Least Squares Feature Extraction
Convert Strings to Factors
Select Terms in a Step Function.
Summarize a Recipe
Moving Window Functions
Orthogonal Polynomial Basis Functions
Shuffle Variables
Sample rows using dplyr
Scaling Numeric Data
Spatial Sign Preprocessing
Create a Profiling Version of a Data Set
Filter rows by position using dplyr
Tidy the Result of a Recipe
Create Dummy Variables using Regular Expressions
Relevel factors to a desired level
Assign missing categories to "unknown"
Convert Ordered Factors to Unordered Factors
Find recommended methods for generating parameter values
Update a recipe step