apply.categorical.interaction.mappings | Applies categorical interacion mappins |
apply.categorical.mappings | Applies categorical mappings |
apply.freq.mappings | Applies frequency mappings |
apply.impute.mappings | Applies imputation mappings |
apply.kmeans.mappings | Applies kmeans feature mappings |
apply.max.scale.mappings | Apply max scaler mappings |
apply.numeric.interactions | Creates numeric interaction features |
apply.outlier.mappings | Applies outlier mapping tables |
automl | Automated machine learning |
data.leak | Data leakage detection |
date.features | Engineers date and time features |
de.duplicate | De-duplicate dataframe |
describe | Tabular exploratory data analysis |
design.pipeline | Design a pipeline for data pre-processing |
detect.feats | Detect feature types |
document | Dynamic documentation for pipeline settings |
eda | Automated exploratory data analysis |
explore.pipelines | Machine learning pipeline optimization |
feature.importance | Random forest feature importance |
lazy.plot | Lazy ggplot plotting |
lazy.predict | Preprocess and predict wrapper |
make.pipeline.grid | Create pipeline grid for optimization |
map.categorical.encoding | Categorical mapping tables |
map.categorical.interactions | Maps categorical interaction combination frames |
map.freq.encoding | Frequency encoding |
map.impute | Imputation mappings |
map.kmeans.features | Kmeans distance to center feature mappings |
map.max.scaler | Maximum value scaler |
map.numeric.interactions | Numeric feature interactions |
map.outliers | Creates mapping tables for detecting outlier values in... |
numeric.transformers | Numeric feature transformations |
pre.process | Pre-processing of data |
quick.format | Minor feature class changes |
text.features | Creates engineered features from text features |
time.partition | Create time sensitive partitions |
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