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