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