recipes-package | recipes: A package for computing and preprocessing design matrices. |
add_check | Add a New Operation to the Current Recipe |
add_role | Manually Add Roles |
add_step | Add a New Operation to the Current Recipe |
all_nominal | Role Selection |
all_numeric | Role Selection |
all_outcomes | Role Selection |
all_predictors | Role Selection |
bake | Apply a Trained Data Recipe |
bake.recipe | Apply a Trained Data Recipe |
biomass | Biomass Data |
check_cols | Check if all Columns are Present |
check_missing | Check for Missing Values |
check_range | Check Range Consistency |
covers | Raw Cover Type Data |
credit_data | Credit Data |
current_info | Role Selection |
denom_vars | Ratio Variable Creation |
detect_step | Detect if a particular step or check is used in a recipe |
discretize | Discretize Numeric Variables |
discretize.default | Discretize Numeric Variables |
discretize.numeric | Discretize Numeric Variables |
dummy_names | Naming Tools |
formula.recipe | Create a Formula from a Prepared Recipe |
fully_trained | Check to see if a recipe is trained/prepared |
has_role | Role Selection |
has_type | Role Selection |
imp_vars | Imputation via Bagged Trees |
juice | Extract Finalized Training Set |
names0 | Naming Tools |
okc | OkCupid Data |
predict.discretize | Discretize Numeric Variables |
prep | Train a Data Recipe |
prep.recipe | Train a Data Recipe |
print.recipe | Print a Recipe |
recipe | Create a Recipe for Preprocessing Data |
recipe.data.frame | Create a Recipe for Preprocessing Data |
recipe.default | Create a Recipe for Preprocessing Data |
recipe.formula | Create a Recipe for Preprocessing Data |
recipe.matrix | Create a Recipe for Preprocessing Data |
recipes | recipes: A package for computing and preprocessing design matrices. |
selections | Methods for Select Variables in Step Functions |
step_bagimpute | Imputation via Bagged Trees |
step_bin2factor | Create a Factors from A Dummy Variable |
step_BoxCox | Box-Cox Transformation for Non-Negative Data |
step_bs | B-Spline Basis Functions |
step_center | Centering Numeric Data |
step_classdist | Distances to Class Centroids |
step_corr | High Correlation Filter |
step_count | Create Counts of Patterns using Regular Expressions |
step_date | Date Feature Generator |
step_depth | Data Depths |
step_discretize | Discretize Numeric Variables |
step_downsample | Down-Sample a Data Set Based on a Factor Variable |
step_dummy | Dummy Variables Creation |
step_factor2string | Convert Factors to Strings |
step_holiday | Holiday Feature Generator |
step_hyperbolic | Hyperbolic Transformations |
step_ica | ICA Signal Extraction |
step_interact | Create Interaction Variables |
step_intercept | Add intercept (or constant) column |
step_inverse | Inverse Transformation |
step_invlogit | Inverse Logit Transformation |
step_isomap | Isomap Embedding |
step_knnimpute | Imputation via K-Nearest Neighbors |
step_kpca | Kernel PCA Signal Extraction |
step_lag | Create a lagged predictor |
step_lincomb | Linear Combination Filter |
step_log | Logarithmic Transformation |
step_logit | Logit Transformation |
step_lowerimpute | Impute Numeric Data Below the Threshold of Measurement |
step_meanimpute | Impute Numeric Data Using the Mean |
step_modeimpute | Impute Nominal Data Using the Most Common Value |
step_naomit | Remove observations with missing values |
step_novel | Simple Value Assignments for Novel Factor Levels |
step_ns | Nature Spline Basis Functions |
step_num2factor | Convert Numbers to Factors |
step_nzv | Near-Zero Variance Filter |
step_ordinalscore | Convert Ordinal Factors to Numeric Scores |
step_other | Collapse Some Categorical Levels |
step_pca | PCA Signal Extraction |
step_pls | Partial Least Squares Feature Extraction |
step_poly | Orthogonal Polynomial Basis Functions |
step_profile | Create a Profiling Version of a Data Set |
step_range | Scaling Numeric Data to a Specific Range |
step_ratio | Ratio Variable Creation |
step_regex | Create Dummy Variables using Regular Expressions |
step_relu | Apply (Smoothed) Rectified Linear Transformation |
step_rm | General Variable Filter |
step_rollimpute | Impute Numeric Data Using a Rolling Window Statistic |
step_scale | Scaling Numeric Data |
step_shuffle | Shuffle Variables |
step_spatialsign | Spatial Sign Preprocessing |
step_sqrt | Square Root Transformation |
step_string2factor | Convert Strings to Factors |
step_unorder | Convert Ordered Factors to Unordered Factors |
step_upsample | Up-Sample a Data Set Based on a Factor Variable |
step_window | Moving Window Functions |
step_YeoJohnson | Yeo-Johnson Transformation |
step_zv | Zero Variance Filter |
summary.recipe | Summarize a Recipe |
terms_select | Select Terms in a Step Function. |
tidy.check_cols | Check if all Columns are Present |
tidy.check_missing | Check for Missing Values |
tidy.check_range | Check Range Consistency |
tidy.recipe | Tidy the Result of a Recipe |
tidy.step_bagimpute | Imputation via Bagged Trees |
tidy.step_bin2factor | Create a Factors from A Dummy Variable |
tidy.step_BoxCox | Box-Cox Transformation for Non-Negative Data |
tidy.step_bs | B-Spline Basis Functions |
tidy.step_center | Centering Numeric Data |
tidy.step_classdist | Distances to Class Centroids |
tidy.step_corr | High Correlation Filter |
tidy.step_count | Create Counts of Patterns using Regular Expressions |
tidy.step_date | Date Feature Generator |
tidy.step_depth | Data Depths |
tidy.step_discretize | Discretize Numeric Variables |
tidy.step_downsample | Down-Sample a Data Set Based on a Factor Variable |
tidy.step_dummy | Dummy Variables Creation |
tidy.step_factor2string | Convert Factors to Strings |
tidy.step_holiday | Holiday Feature Generator |
tidy.step_hyperbolic | Hyperbolic Transformations |
tidy.step_ica | ICA Signal Extraction |
tidy.step_interact | Create Interaction Variables |
tidy.step_inverse | Inverse Transformation |
tidy.step_invlogit | Inverse Logit Transformation |
tidy.step_isomap | Isomap Embedding |
tidy.step_knnimpute | Imputation via K-Nearest Neighbors |
tidy.step_kpca | Kernel PCA Signal Extraction |
tidy.step_lincomb | Linear Combination Filter |
tidy.step_log | Logarithmic Transformation |
tidy.step_logit | Logit Transformation |
tidy.step_lowerimpute | Impute Numeric Data Below the Threshold of Measurement |
tidy.step_meanimpute | Impute Numeric Data Using the Mean |
tidy.step_modeimpute | Impute Nominal Data Using the Most Common Value |
tidy.step_naomit | Remove observations with missing values |
tidy.step_novel | Simple Value Assignments for Novel Factor Levels |
tidy.step_ns | Nature Spline Basis Functions |
tidy.step_num2factor | Convert Numbers to Factors |
tidy.step_nzv | Near-Zero Variance Filter |
tidy.step_ordinalscore | Convert Ordinal Factors to Numeric Scores |
tidy.step_other | Collapse Some Categorical Levels |
tidy.step_pca | PCA Signal Extraction |
tidy.step_pls | Partial Least Squares Feature Extraction |
tidy.step_poly | Orthogonal Polynomial Basis Functions |
tidy.step_profile | Create a Profiling Version of a Data Set |
tidy.step_range | Scaling Numeric Data to a Specific Range |
tidy.step_ratio | Ratio Variable Creation |
tidy.step_regex | Create Dummy Variables using Regular Expressions |
tidy.step_relu | Apply (Smoothed) Rectified Linear Transformation |
tidy.step_rm | General Variable Filter |
tidy.step_rollimpute | Impute Numeric Data Using a Rolling Window Statistic |
tidy.step_scale | Scaling Numeric Data |
tidy.step_shuffle | Shuffle Variables |
tidy.step_spatialsign | Spatial Sign Preprocessing |
tidy.step_sqrt | Square Root Transformation |
tidy.step_string2factor | Convert Strings to Factors |
tidy.step_unorder | Convert Ordered Factors to Unordered Factors |
tidy.step_upsample | Up-Sample a Data Set Based on a Factor Variable |
tidy.step_window | Moving Window Functions |
tidy.step_YeoJohnson | Yeo-Johnson Transformation |
tidy.step_zv | Zero Variance Filter |