Helper Functions for Profile Data Sets
Check to see if a recipe is trained/prepared
Check range consistency
Get types for use in recipes
Discretize Numeric Variables
Quantitatively check on variables
Detect if a particular step or check is used in a recipe
Developer functions for creating recipes steps
Helpers for printing step functions
Create a formula from a prepared recipe
Print a Recipe
Role Selection
Extract transformed training set
Estimate a preprocessing recipe
Naming Tools
Make a random identification field for steps
Wrapper function for preparing recipes within resampling
Create a recipe for preprocessing data
Internal Functions
Get the keep_original_cols
value of a recipe step
Checks that steps have all S3 methods
Removes original columns if options apply
Manually alter roles
Evaluate a selection with tidyselect semantics specific to recipes
recipes: A package for computing and preprocessing design matrices.
required_pkgs.step_classdist_shrunken
S3 methods for tracking which additional packages are needed for steps.
Removes columns if options apply
Update packages
Methods for selecting variables in step functions
Objects exported from other packages
Create a factors from A dummy variable
High correlation filter
Sort rows using dplyr
B-spline basis functions
Yeo-Johnson transformation
step
sets the class of the step
and check
is for checks.
Compute shrunken centroid distances for classification models
Distances to class centroids
Box-Cox transformation for non-negative data
Centering numeric data
Date feature generator
Handle levels in multiple predictors together
Data depths
Extract patterns from nominal data
Discretize Numeric Variables
Filter rows using dplyr
Create traditional dummy variables
Create counts of patterns using regular expressions
Cut a numeric variable into a factor
Convert factors to strings
Hyperbolic transformations
Add sin and cos terms for harmonic analysis
Impute numeric variables via a linear model
Impute via bagged trees
Holiday feature generator
Distance between two locations
Missing value column filter
Impute via k-nearest neighbors
Impute numeric data below the threshold of measurement
Create interaction variables
Add intercept (or constant) column
Inverse transformation
Inverse logit transformation
ICA signal extraction
Convert values to predefined integers
Create missing data column indicators
Impute nominal data using the most common value
Impute numeric data using a rolling window statistic
Impute numeric data using the mean
Impute numeric data using the median
Mutate multiple columns using dplyr
Add new variables using dplyr
Logit transformation
Logarithmic transformation
Isomap embedding
Radial basis function kernel PCA signal extraction
Polynomial kernel PCA signal extraction
Kernel PCA signal extraction
Convert numbers to factors
Near-zero variance filter
Non-negative matrix factorization signal extraction
Remove observations with missing values
Non-negative matrix factorization signal extraction with lasso penalization
Linear combination filter
Simple value assignments for novel factor levels
Convert ordinal factors to numeric scores
Create a lagged predictor
Center and scale numeric data
Collapse infrequent categorical levels
Natural spline basis functions
Orthogonal polynomial basis functions
Scaling numeric data to a specific range
Create a profiling version of a data set
Partial least squares feature extraction
Detect a regular expression
Percentile transformation
Generalized bernstein polynomial basis
Ratio variable creation
Relevel factors to a desired level
PCA signal extraction
Rename variables by name using dplyr
Scaling mumeric data
Shuffle variables
Sample rows using dplyr
Rename multiple columns using dplyr
Filter rows by position using dplyr
General variable filter
Spatial sign preprocessing
Apply (smoothed) rectified linear transformation
Select variables using dplyr
Monotone splines
Non-negative splines
Natural splines
Time feature generator
Convert strings to factors
Basis splines
Assign missing categories to "unknown"
Update role specific requirements
Square root transformation
Select terms in a step function.
Summarize a recipe
Convert ordered factors to unordered factors
Convex splines
Update a recipe step
Tidy the result of a recipe
Moving window functions
Zero variance filter
Add a New Operation to the Current Recipe
Check variable class
Using case weights with recipes
Helpers for steps with case weights
Check for new values
Check for required column at bake-time
Check if all columns are present
check that newly created variable names don't overlap
Check for missing values
Apply a trained preprocessing recipe