Expand description
Data transformation module for SciRS2
This module provides utilities for transforming data in ways that are useful for machine learning and data analysis. The main functionalities include:
- Data normalization and standardization
- Feature engineering
- Dimensionality reduction
Re-exports§
pub use encoding::BinaryEncoder;pub use encoding::OneHotEncoder;pub use encoding::OrdinalEncoder;pub use encoding::TargetEncoder;pub use error::Result;pub use error::TransformError;pub use features::binarize;pub use features::discretize_equal_frequency;pub use features::discretize_equal_width;pub use features::log_transform;pub use features::power_transform;pub use features::PolynomialFeatures;pub use features::PowerTransformer;pub use impute::DistanceMetric;pub use impute::ImputeStrategy;pub use impute::IterativeImputer;pub use impute::KNNImputer;pub use impute::MissingIndicator;pub use impute::SimpleImputer;pub use impute::WeightingScheme;pub use normalize::normalize_array;pub use normalize::normalize_vector;pub use normalize::NormalizationMethod;pub use normalize::Normalizer;pub use reduction::trustworthiness;pub use reduction::TruncatedSVD;pub use reduction::LDA;pub use reduction::PCA;pub use reduction::TSNE;pub use scaling::MaxAbsScaler;pub use scaling::QuantileTransformer;pub use selection::VarianceThreshold;
Modules§
- encoding
- Categorical data encoding utilities Categorical data encoding utilities
- error
- Error handling for the transformation module Error types for the data transformation module
- features
- Feature engineering techniques Feature engineering utilities
- impute
- Missing value imputation utilities Missing value imputation utilities
- normalize
- Basic normalization methods for data Data normalization and standardization utilities
- reduction
- Dimensionality reduction algorithms Dimensionality reduction techniques
- scaling
- Advanced scaling and transformation methods Advanced scaling and transformation methods
- selection
- Feature selection utilities Feature selection utilities