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Naive Bayes classifiers
This module provides various Naive Bayes classifiers for different types of features, compatible with scikit-learn’s naive_bayes module.
Modules§
- advanced_
numerical_ stability - Numerical stability improvements
- cache_
optimization - Cache-friendly data layouts and operations
- const_
utils - Const generic utilities for fixed-size models
- cv_
utils - Utility functions for image processing and computer vision
- distribution_
types - Distribution type markers
- feature_
types - Feature type markers
- memory_
efficient - Memory-efficient operations
- numerical_
stability - Extended numerical stability utilities for Naive Bayes computations
- parallel
- Parallel parameter estimation
- prob_
utils - Type-safe probability computation utilities
- profile_
guided_ optimization - Profile-guided optimization for adaptive performance tuning
- simd
- SIMD-optimized probability computations
- unsafe_
optimizations - Unsafe performance optimizations for critical paths
Structs§
- AdaBoost
Naive Bayes - Adaptive Boosting (AdaBoost) for Naive Bayes classifiers
- Adaptive
Smoothing - Adaptive smoothing implementation
- Advanced
Chain Classifier - Advanced chain classifier with sophisticated label ordering
- AdvancedKDE
- Kernel Density Estimator for non-parametric probability estimation
- Auto
Feature Transformer - Core auto feature transformer
- Auto
Pipeline Config - Configuration for automated preprocessing pipelines
- Auto
Transform Config - Configuration for automated transformations
- Automated
Preprocessing Pipeline - Automated preprocessing pipeline
- Averaging
Naive Bayes - Model averaging ensemble
- BANConfig
- Configuration for Bayesian Network Augmented Naive Bayes
- Bagged
Naive Bayes - Bagged Naive Bayes ensemble
- Base
Distribution Params - Base distribution parameters for Dirichlet Process
- Bayesian
Network - Bayesian network structure for feature dependencies
- Bayesian
Network AugmentedNB - Bayesian Network Augmented Naive Bayes classifier
- Bayesian
Uncertainty - Bayesian uncertainty quantification using Monte Carlo sampling
- Benchmark
Config - Benchmark configuration
- Benchmark
Results - Benchmark results comparing sklears and scikit-learn
- BernoulliNB
- Bernoulli Naive Bayes classifier
- BetaNB
- Beta Naive Bayes classifier
- Biomarker
DiscoveryNB - Biomarker discovery using Naive Bayes
- Bootstrap
Config - Bootstrap sampling strategy for bagging
- CSVChunk
Reader - Out-of-core data reader that reads data from a CSV file in chunks
- Calibration
Method - Calibration methods for improving probability estimates
- Calibration
Metrics - Calibration metrics for probabilistic predictions
- CategoricalNB
- Categorical Naive Bayes classifier
- Causal
Discovery - Causal discovery algorithms
- Causal
Discovery Config - Causal
Graph - Causal graph representation
- Causal
Naive Bayes - Causal Naive Bayes integrating all causal inference methods
- Class
Hierarchy - Class hierarchy structure
- ComplementNB
- Complement Naive Bayes classifier
- CompressedNB
Model - Compressed model representation for memory-efficient storage
- Compression
Metadata - Metadata for compression parameters
- Concept
Drift Detector - Concept drift detector
- Counterfactual
Reasoning - Counterfactual reasoning
- Cross
Validation Selector - Cross-validation for hyperparameter selection
- Data
Characteristics - Data
Validation Config - Configuration for data validation
- Deep
Generative Config - Deep
Generative Naive Bayes - Deep Generative Naive Bayes combining all methods
- Dependency
Tree - Tree structure for feature dependencies
- Dirichlet
Component - Component in the Dirichlet Process mixture
- Dirichlet
Process Config - Configuration for Dirichlet Process Naive Bayes
- Dirichlet
ProcessNB - Dirichlet Process Naive Bayes classifier
- DoCalculus
- Do-calculus operations
- ELBO
Components - Evidence Lower Bound (ELBO) components
- Empirical
Bayes Estimator - Empirical Bayes estimation for hyperparameters
- Ensemble
Predictions - Ensemble predictions with uncertainty estimates
- Estimation
Result - Exponential
FamilyNB - Exponential Family Naive Bayes classifier
- Exponential
FamilyNB Config - Configuration for Exponential Family Naive Bayes
- Feature
Dependency - Feature dependency representation
- Feature
Interaction Detector - Core feature interaction detector
- Feature
Selection Results - Results from feature selection process
- Fixed
Size Model - Fixed-size model configuration using const generics
- FlexibleNB
- Flexible Naive Bayes classifier with adaptive distributions
- FlexibleNB
Config - Configuration for Flexible Naive Bayes
- Flow
Config - Fluent
BernoulliNB - Fluent wrapper for Bernoulli Naive Bayes
- Fluent
CategoricalNB - Fluent wrapper for Categorical Naive Bayes
- Fluent
ComplementNB - Fluent wrapper for Complement Naive Bayes
- Fluent
GaussianNB - Fluent wrapper for Gaussian Naive Bayes
- Fluent
MultinomialNB - Fluent wrapper for Multinomial Naive Bayes
- Fluent
PoissonNB - Fluent wrapper for Poisson Naive Bayes
- GammaNB
- Gamma Naive Bayes classifier
- Gaussian
Estimator - Gaussian distribution parameter estimator
- GaussianNB
- Gaussian Naive Bayes classifier
- Gaussian
Process - Gaussian Process for probabilistic modeling
- Gene
Expression Config - Gene
ExpressionNB - Gene expression analysis using Naive Bayes
- GenomicNB
Config - Configuration for genomic sequence classification
- Genomic
Naive Bayes - Genomic sequence Naive Bayes classifier
- Genomic
Sequence - Genomic sequence representation
- Goodness
OfFit Results - Goodness-of-fit test results
- HMMConfig
- Hidden Markov Model configuration for time series classification
- HMMNaive
Bayes - Hidden Markov Model integrated with Naive Bayes for time series classification
- Hierarchical
Config - Configuration for Hierarchical Naive Bayes
- HierarchicalNB
- Hierarchical Naive Bayes classifier
- Hierarchy
Node - Node in the class hierarchy
- Hyperparameter
Optimizer - Hyperparameter optimization for multiple smoothing methods
- Image
Data - Image representation and preprocessing utilities
- Image
Metadata - ImageNB
Config - Configuration for image-based Naive Bayes classifiers
- Image
Naive Bayes - Image classification Naive Bayes classifier
- Instrumental
Variables - Instrumental Variables estimation
- Interaction
Results - Results from feature interaction detection
- KDEConfig
- Configuration for kernel density estimation
- Kernel
Density Estimator - Kernel density estimator for a single feature
- Kernel
Naive Bayes - Kernel-based Naive Bayes classifier
- Kernel
Parameter Learner - Kernel parameter learning through cross-validation
- LDATopic
Model - Simple Latent Dirichlet Allocation (LDA) topic model for feature extraction
- Label
Correlation Analysis - Label correlation analysis for understanding relationships between labels
- Label
Dependency Graph - Label dependency modeling
- Label
Hierarchy - Hierarchical label structure
- Lazy
Loaded Model - Lazy-loading wrapper for large models
- Memory
Chunk Iterator - Memory-based chunk iterator for testing
- Memory
MappedNB Model - Memory-mapped parameter storage for very large models
- Memory
Optimized Ops - Memory-efficient operations for large datasets
- Memory
Stats - Memory usage statistics
- MixedNB
- Mixed Naive Bayes classifier
- Model
Candidate - Model
Criticism Results - Model criticism metrics
- Model
Degradation Metrics - Model degradation monitoring metrics
- Model
Uncertainty Propagation - Model uncertainty propagation methods
- Multi
LabelNB - Multi-label Naive Bayes classifier
- Multinomial
Estimator - Multinomial distribution parameter estimator
- MultinomialNB
- Multinomial Naive Bayes classifier
- NGram
Extractor - N-gram feature extractor for text
- NPEConfig
- Naive
Bayes Benchmark - Comprehensive benchmark suite
- Naive
Bayes Builder - Enhanced fluent API builder for Naive Bayes models
- Neighborhood
Stats - Network
Edge - Edge in the Bayesian network representing conditional dependencies
- Neural
Layer - Neural network layer
- NeuralNB
Builder - Builder for Neural Naive Bayes configuration
- NeuralNB
Config - Neural Naive Bayes network configuration
- Neural
Naive Bayes - Neural Naive Bayes classifier
- Neural
Posterior Estimator - Neural Posterior Estimation
- NonparametricNB
- Non-parametric Naive Bayes classifier
- NonparametricNB
Config - Configuration for Non-parametric Naive Bayes
- Normalizing
Flow - Normalizing Flow for distribution learning
- Online
Gaussian Stats - Online Gaussian Naive Bayes statistics
- Online
Learning Config - Configuration for online learning
- Online
Multinomial Stats - Online Multinomial Naive Bayes statistics
- Online
Naive Bayes - Online/Streaming Naive Bayes classifier
- OutOf
Core Naive Bayes - Out-of-core Naive Bayes classifier
- PPCResults
- Posterior predictive checking results
- Phylogenetic
Config - PhylogeneticNB
- Phylogenetic classification using Naive Bayes
- Plugin
Registry - Registry for managing pluggable components
- PoissonNB
- Poisson Naive Bayes classifier
- Prediction
Context - Prediction
Stability Metrics - Prediction stability metrics
- Predictive
Accuracy Assessment - Predictive accuracy assessment metrics
- Predictive
Accuracy Assessor - Predictive accuracy assessor
- Probabilistic
Validator - Probabilistic model validator
- Protein
Structure Config - Protein
StructureNB - Protein structure prediction using Naive Bayes
- RKHS
Feature Selector - RKHS-based feature selection using kernel methods
- Reliability
Tests - Statistical tests for model reliability
- Reproducing
Kernel Hilbert Space - Reproducing Kernel Hilbert Space (RKHS) implementation
- Residual
Analysis - Residual analysis for probabilistic models
- Semi
Naive Bayes - Semi-Naive Bayes classifier
- Semi
Naive Bayes Config - Configuration for Semi-Naive Bayes
- Sequence
Metadata - SerializableNB
Params - Serializable model parameters for all Naive Bayes variants
- Sparse
Matrix - Sparse matrix representation for efficient computation
- Sparse
Row - Sparse row representation
- Spatial
Model - SpatialNB
Config - Spatial
Naive Bayes - Spatial Naive Bayes for analyzing spatial relationships in images
- Stacking
Naive Bayes - Stacking ensemble for Naive Bayes classifiers
- Standard
Uncertainty - Standard uncertainty quantification implementation
- Stick
Breaking - Stick-breaking representation for Dirichlet Process
- Streaming
Buffer - Streaming data buffer for managing incoming samples
- Streaming
TemporalNB - Streaming Temporal Naive Bayes for online classification
- Sufficient
Statistics - Sufficient statistics for exponential family distributions
- TANConfig
- Configuration for Tree-Augmented Naive Bayes
- Temporal
Config - Configuration for temporal Naive Bayes models
- Temporal
Feature Extractor - Temporal feature extractor for time series data
- Temporal
Naive Bayes - Temporal Naive Bayes classifier for time series
- Temporal
Validation Results - Temporal validation results for time-dependent data
- Text
MultinomialNB - Text-optimized Multinomial Naive Bayes
- Text
MultinomialNB Config - Configuration for TextMultinomialNB
- Text
Preprocessor - Text preprocessing pipeline for Naive Bayes
- TfIdf
Transformer - TF-IDF (Term Frequency-Inverse Document Frequency) transformer
- Topic
Augmented Config - Configuration for TopicAugmentedTextClassifier
- Topic
Augmented Text Classifier - Text classifier with topic model features
- Tree
AugmentedNB - Tree-Augmented Naive Bayes classifier
- Tree
Edge - Edge in the dependency tree representing feature dependencies
- Typed
Probability - Probability wrapper with compile-time type safety
- Uncertainty
Decomposition - Uncertainty decomposition into epistemic and aleatoric components
- Uncertainty
Measures - Uncertainty types for Naive Bayes predictions
- VAEConfig
- ValidationCV
Results - Cross-validation results for probabilistic models
- Variational
Autoencoder - Variational Autoencoder for generative Naive Bayes
- Variational
Bayes Config - Configuration for Variational Bayes Naive Bayes
- Variational
BayesNB - Variational Bayes Naive Bayes classifier
- Variational
Parameters - Variational parameters for different distributions
- Vectorized
Ops - Vectorized operations for probability computations
- Voting
Naive Bayes - Voting ensemble for Naive Bayes classifiers
Enums§
- Activation
Function - Activation functions for neural networks
- Adaptive
Information Criterion - Information criteria for model selection
- Adaptive
Smoothing Method - Adaptive smoothing method that selects optimal smoothing parameters
- Advanced
Kernel Type - Kernel types for kernel-based Naive Bayes methods
- Amino
Acid - Amino acid encoding for protein sequences
- Auto
Feature Type - Enumeration of feature types
- BANError
- Bandwidth
Method - Bandwidth selection methods
- Bioinformatics
Error - Boosting
Strategy - Boosting strategy for Naive Bayes ensembles
- Causal
Discovery Algorithm - Causal
Inference Error - Chain
Ordering Strategy - Strategy for ordering labels in classifier chains
- Color
Space - Computer
Vision Error - Covariance
Type - Covariance type for HMM emission probabilities
- Data
Type - Supporting types and structures
- Deep
Generative Error - Dependency
Selection Method - Method for selecting feature dependencies
- Dirichlet
Process Error - Distribution
- Supported distributions for adaptive selection
- Distribution
Params - Distribution parameters for flexible NB
- Exponential
Family - Exponential family distribution types
- Feature
Distribution - Feature distribution type for Mixed Naive Bayes
- Feature
Selection Method - Supported feature selection methods
- Feature
Type - Hierarchical
Error - Imbalance
Handling Method - Supported imbalance handling methods
- Inference
Method - Interaction
Method - Supported interaction detection methods
- Kernel
Bandwidth Method - Methods for automatic bandwidth selection
- Kernel
Error - Kernel
Scoring Metric - Kernel
Type - Kernel types for density estimation
- Missing
Value Strategy - Supported missing value imputation strategies
- Multi
Label Strategy - Strategy for multi-label classification
- Naive
Bayes Preset - Configuration presets for common use cases
- Natural
Parameters - Natural parameters for exponential family distributions
- NeuralNB
Error - Nucleotide
- Nucleotide encoding for DNA/RNA sequences
- Online
Learning Error - Outlier
Detection Method - Supported outlier detection methods
- Parameter
Estimation Method - Method for estimating natural parameters
- Prediction
Strategy - Scoring
Method - Secondary
Structure - Protein secondary structure: Helix, Sheet, Coil
- Selection
Criterion - Selection
Method - Method for selecting the best distribution
- Sequence
Type - Sequence types for bioinformatics analysis
- Smoothing
Method - Smoothing method enumeration
- Statistical
Test - Supported statistical tests
- Structure
Learning Method - TANError
- Transform
Method - Supported transformation methods
- ValidationCV
Strategy - Cross-validation strategy for probabilistic models
- Validation
Error - Validation
Scoring Metric - Variational
Error - Variational
Inference Method - Voting
Strategy - Voting strategy for ensemble methods
Traits§
- Composable
Smoothing Method - Trait for composable smoothing methods
- Data
Chunk Iterator - Trait for data sources that can provide chunks of data
- Extensible
Parameter Estimator - Trait for extensible parameter estimation methods
- Flexible
Model Selector - Trait for flexible model selection systems
- Fluent
Naive Bayes Model - Common trait for all fluent Naive Bayes models
- Naive
Bayes Estimator - Trait for Naive Bayes estimators that can be used in ensembles
- Naive
Bayes Mixin - Base trait for Naive Bayes classifiers
- Parameter
Estimator - Trait for parameter estimation
- Pluggable
Distribution - Trait for probability distributions that can be plugged into Naive Bayes models
- Prediction
Middleware - Trait for middleware components in prediction pipelines
- Probabilistic
Model - Trait for probabilistic models that can be validated
- Type
Safe Probabilistic Model - Zero-cost probabilistic abstractions
- Typed
Probability Ops - Type-safe probability operations
- Uncertainty
Quantification - Uncertainty quantification methods
- Validate
Feature Type - Compile-time feature type validation
Functions§
- enhanced_
log - Enhanced logarithm function with better numerical stability
- get_
global_ registry - Get a reference to the global plugin registry
- log_
sum_ exp - Log-sum-exp function for numerical stability in probability computations
- naive_
bayes - Convenience function to create a new Naive Bayes builder
- naive_
bayes_ preset - Convenience function to create a new Naive Bayes builder with preset
- normalize_
log_ probs - Normalize log probabilities to probabilities with numerical stability
- register_
distribution - Register a custom distribution globally
- register_
middleware - Register middleware globally
- register_
model_ selector - Register a custom model selector globally
- register_
parameter_ estimator - Register a custom parameter estimator globally
- register_
smoothing_ method - Register a custom smoothing method globally