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