MultinomialNb / Classifier Layer

Multinomial Naive Bayes Classifier - Naive Bayes classifier for multinomial models. Specialized for discrete count features.

Mathematical form: $P (y ∣ x) \propto P (y) i = 1 \prod n P (x_{i} ∣ y)$ where: $P (x_{i} ∣ y) = \frac{( α + N _{y i} )}{( α n + N _{y} )}$

Key characteristics:

Designed for count data
Integer feature assumption
Multinomial distribution model
Additive smoothing support
Efficient computation

Common applications:

Text classification (word counts)
Document categorization
Term frequency analysis
Bag-of-words models
Event counting scenarios

Outputs:

Predicted Table: Input data with predictions
Validation Results: Cross-validation metrics
Test Metric: Test set performance
ROC Curve Data: ROC analysis information
Confusion Matrix: Classification breakdown
Feature Importances: Feature likelihood parameters

Note: Features should represent counts or frequencies of events

Table

Predicted Table

Validation Results

Test Metric

ROC Curve Data

Confusion Matrix

Feature Importances

SelectFeatures

[column, ...]

Feature columns for Multinomial Naive Bayes:

Requirements:

Non-negative integer counts
No missing values
Discrete frequency data
Meaningful count features

Preprocessing guidelines:

Feature extraction:
- Convert to count data
- Term frequency calculation
- Event counting
Data preparation:
- Ensure non-negativity
- Handle sparse data
- Normalize if needed
Quality checks:
- Verify count nature
- Check sparsity levels
- Monitor feature distribution

If empty, uses all numeric columns except target.

SelectTarget

column

Target column for classification:

Requirements:

Categorical labels
No missing values
At least two classes
Properly encoded

Model characteristics:

Class-conditional count modeling
Learns feature distributions
Supports multi-class naturally

Quality checks:

Validate label consistency
Check class distribution
Monitor rare classes
Verify label meanings

Params

oneof

DefaultParams

Standard configuration for Multinomial Naive Bayes:

Default settings:

Alpha: 1.0 (Laplace smoothing)
Force alpha: True (exact smoothing)
Fit prior: True (learn class priors)

Best suited for:

Text classification
Count-based features
Discrete event data
Initial modeling

Provides robust baseline for count data problems

CustomParams

Fine-grained control over Multinomial Naive Bayes parameters:

Parameter groups:

Smoothing control
Prior probability settings
Numerical stability options

Alpha

f64

Additive (Laplace/Lidstone) smoothing parameter:

Effect on probabilities: $P (x_{i} ∣ y) = \frac{co u n t ( x _{i} ∣ y ) + α}{co u n t ( y ) + α n}$

Typical values:

1.0: Laplace smoothing
0.0: No smoothing (with force_alpha)
(0,1): Lidstone smoothing

Prevents zero probabilities in predictions

ForceAlpha

bool

true

Alpha value enforcement control:

Behavior:

true: Use exact alpha value
false: Minimum 1e-10 threshold

Important for numerical stability

FitPrior

bool

true

Whether to learn class prior probabilities or not. If false, a uniform prior will be used.

When enabled: $P (y) = \frac{N _{y} + α}{N + α k}$ When disabled: $P (y) = \frac{1}{k}$

where k is number of classes

Use true when class distribution matters

GridSearch

Hyperparameter optimization for Multinomial Naive Bayes:

Search strategy:

Focus on smoothing parameter
Prior probability options
Stability settings

Best practices:

Consider data sparsity
Monitor zero probabilities
Balance smoothing effects

Computational complexity:

Time: $O (n_{co mbina t i o n s} \cdot n_{s am pl es} \cdot n_{f e a t u res})$
Memory: $O (n_{co mbina t i o n s} \cdot n_{c l a sses} \cdot n_{f e a t u res})$

Alpha

[f64, ...]

Smoothing parameter values to evaluate:

Common grids:

Standard: [0.1, 1.0, 10.0]
Fine-grained: [0.5, 1.0, 2.0]
Wide range: [0.01, 0.1, 1.0, 10.0]

Selection strategy:

Higher values: More smoothing, sparse data
Lower values: Less smoothing, dense data
Consider feature sparsity pattern

ForceAlpha

[bool, ...]

true

Alpha enforcement options to evaluate:

Combinations:

[true]: Exact alpha values
[false]: Minimum 1e-10 threshold
[true, false]: Compare both approaches

Consider:

Numerical stability needs
Very small alpha values
Data quality issues

FitPrior

[bool, ...]

true

Prior probability learning options:

Combinations:

[true]: Learn from data distribution
[false]: Use uniform priors
[true, false]: Compare both approaches

Important when:

Classes are imbalanced
Prior knowledge available
Testing assumption impact

RefitScore

enum

Accuracy

Performance metric for model evaluation:

Selection criteria:

Default: Model's built-in scoring
Accuracy: Overall correctness
BalancedAccuracy: For imbalanced data
LogLoss: Probability quality
RocAuc: Threshold-independent

Choose based on:

Class distribution
Problem requirements
Prediction type needed

Default ~

Uses estimator's built-in scoring method:

For Bernoulli NB:

Returns accuracy score
Equal weight to all samples
Fast computation

Best for:

Quick evaluation
Balanced datasets
Initial testing

Accuracy ~

Standard classification accuracy score:

Formula: $Accuracy = \frac{TP + TN}{TP + TN + FP + FN}$

Properties:

Range: [0, 1]
Perfect score: 1.0
Baseline: max(class proportions)

Best for:

Balanced classes
Equal error costs
Simple evaluation

BalancedAccuracy ~

Class-weighted accuracy score:

Formula: $Balanced Accuracy = \frac{1}{n _{c l a sses}} i = 1 \sum n_{c l a sses} \frac{T P _{i}}{T P _{i} + F N _{i}}$

Properties:

Adjusts for class imbalance
Range: [0, 1]
Random baseline: 0.5

Best for:

Imbalanced datasets
When minority classes matter
Uneven class distributions

LogLoss ~

Logarithmic loss (cross-entropy):

Formula: $- \frac{1}{N} i = 1 \sum N j = 1 \sum M y_{ij} lo g (p_{ij})$

Properties:

Penalizes confident mistakes
Range: [0, ∞)
Perfect score: 0.0

Best for:

Probability calibration
When confidence matters
Probabilistic predictions

RocAuc ~

Area Under Receiver Operating Characteristic Curve:

Properties:

Threshold-independent
Range: [0, 1]
Random baseline: 0.5
Perfect score: 1.0

Best for:

Binary classification
Threshold tuning
Ranking evaluation
Imbalanced datasets

Note: For multiclass, computes average ROC AUC

Split

oneof

DefaultSplit

Standard train-test split configuration optimized for general classification tasks.

Configuration:

Test size: 20% (0.2)
Random seed: 98
Shuffling: Enabled
Stratification: Based on target distribution

Advantages:

Preserves class distribution
Provides reliable validation
Suitable for most datasets

Best for:

Medium to large datasets
Independent observations
Initial model evaluation

Splitting uses the ShuffleSplit strategy or StratifiedShuffleSplit strategy depending on the field stratified. Note: If shuffle is false then stratified must be false.

CustomSplit

Configurable train-test split parameters for specialized requirements. Allows fine-tuning of data division strategy for specific use cases or constraints.

Use cases:

Time series data
Grouped observations
Specific train/test ratios
Custom validation schemes

RandomState

u64

Random seed for reproducible splits. Ensures:

Consistent train/test sets
Reproducible experiments
Comparable model evaluations

Same seed guarantees identical splits across runs.

Shuffle

bool

true

Data shuffling before splitting. Effects:

true: Randomizes order, better for i.i.d. data
false: Maintains order, important for time series

When to disable:

Time dependent data
Sequential patterns
Grouped observations

TrainSize

f64

0.8

Proportion of data for training. Considerations:

Larger (e.g., 0.8-0.9): Better model learning
Smaller (e.g., 0.5-0.7): Better validation

Common splits:

0.8: Standard (80/20 split)
0.7: More validation emphasis
0.9: More training emphasis

Stratified

bool

false

Maintain class distribution in splits. Important when:

Classes are imbalanced
Small classes present
Representative splits needed

Requirements:

Classification tasks only
Cannot use with shuffle=false
Sufficient samples per class

Cv

oneof

DefaultCv

Standard cross-validation configuration using stratified 3-fold splitting.

Configuration:

Folds: 3
Method: StratifiedKFold
Stratification: Preserves class proportions

Advantages:

Balanced evaluation
Reasonable computation time
Good for medium-sized datasets

Limitations:

May be insufficient for small datasets
Higher variance than larger fold counts
May miss some data patterns

CustomCv

Configurable stratified k-fold cross-validation for specific validation requirements.

Features:

Adjustable fold count with NFolds determining the number of splits.
Stratified sampling
Preserved class distributions

Use cases:

Small datasets (more folds)
Large datasets (fewer folds)
Detailed model evaluation
Robust performance estimation

NFolds

u32

Number of cross-validation folds. Guidelines:

3-5: Large datasets, faster training
5-10: Standard choice, good balance
10+: Small datasets, thorough evaluation

Trade-offs:

More folds: Better evaluation, slower training
Fewer folds: Faster training, higher variance

Must be at least 2.

KfoldCv

K-fold cross-validation without stratification. Divides data into k consecutive folds for iterative validation.

Process:

Splits data into k equal parts
Each fold serves as validation once
Remaining k-1 folds form training set

Use cases:

Regression problems
Large, balanced datasets
When stratification unnecessary
Continuous target variables

Limitations:

May not preserve class distributions
Less suitable for imbalanced data
Can create biased splits with ordered data

NSplits

u32

Number of folds for cross-validation. Selection guide: Recommended values:

5: Standard choice (default)
3: Large datasets/quick evaluation
10: Thorough evaluation/smaller datasets

Trade-offs:

Higher values: More thorough, computationally expensive
Lower values: Faster, potentially higher variance

Must be at least 2 for valid cross-validation.

RandomState

u64

Random seed for fold generation when shuffling. Important for:

Reproducible results
Consistent fold assignments
Benchmark comparisons
Debugging and validation

Set specific value for reproducibility across runs.

Shuffle

bool

true

Whether to shuffle data before splitting into folds. Effects:

true: Randomized fold composition (recommended)
false: Sequential splitting

Enable when:

Data may have ordering
Better fold independence needed

Disable for:

Time series data
Ordered observations

StratifiedKfoldCv

Stratified K-fold cross-validation maintaining class proportions across folds.

Key features:

Preserves class distribution in each fold
Handles imbalanced datasets
Ensures representative splits

Best for:

Classification problems
Imbalanced class distributions
When class proportions matter

Requirements:

Classification tasks only
Sufficient samples per class
Categorical target variable

NSplits

u32

Number of stratified folds. Guidelines: Typical values:

5: Standard for most cases
3: Quick evaluation/large datasets
10: Detailed evaluation/smaller datasets

Considerations:

Must allow sufficient samples per class per fold
Balance between stability and computation time
Consider smallest class size when choosing

RandomState

u64

Seed for reproducible stratified splits. Ensures:

Consistent fold assignments
Reproducible results
Comparable experiments
Systematic validation

Fixed seed guarantees identical stratified splits.

Shuffle

bool

false

Data shuffling before stratified splitting. Impact:

true: Randomizes while maintaining stratification
false: Maintains data order within strata

Use cases:

true: Independent observations
false: Grouped or sequential data

Class proportions maintained regardless of setting.

ShuffleSplitCv

Random permutation cross-validator with independent sampling.

Characteristics:

Random sampling for each split
Independent train/test sets
More flexible than K-fold
Can have overlapping test sets

Advantages:

Control over test size
Fresh splits each iteration
Good for large datasets

Limitations:

Some samples might never be tested
Others might be tested multiple times
No guarantee of complete coverage

NSplits

u32

Number of random splits to perform. Consider: Common values:

5: Standard evaluation
10: More thorough assessment
3: Quick estimates

Trade-offs:

More splits: Better estimation, longer runtime
Fewer splits: Faster, less stable estimates

Balance between computation and stability.

RandomState

u64

Random seed for reproducible shuffling. Controls:

Split randomization
Sample selection
Result reproducibility

Important for:

Debugging
Comparative studies
Result verification

TestSize

f64

0.2

Proportion of samples for test set. Guidelines: Common ratios:

0.2: Standard (80/20 split)
0.25: More validation emphasis
0.1: More training data

Considerations:

Dataset size
Model complexity
Validation requirements

It must be between 0.0 and 1.0.

StratifiedShuffleSplitCv

Stratified random permutation cross-validator combining shuffle-split with stratification.

Features:

Maintains class proportions
Random sampling within strata
Independent splits
Flexible test size

Ideal for:

Imbalanced datasets
Large-scale problems
When class distributions matter
Flexible validation schemes

NSplits

u32

Number of stratified random splits. Guidelines: Recommended values:

5: Standard evaluation
10: Detailed analysis
3: Quick assessment

Consider:

Sample size per class
Computational resources
Stability requirements

RandomState

u64

Seed for reproducible stratified sampling. Ensures:

Consistent class proportions
Reproducible splits
Comparable experiments

Critical for:

Benchmarking
Research studies
Quality assurance

TestSize

f64

0.2

Fraction of samples for stratified test set. Best practices: Common splits:

0.2: Balanced evaluation
0.3: More thorough testing
0.15: Preserve training size

Consider:

Minority class size
Overall dataset size
Validation objectives

It must be between 0.0 and 1.0.

Time Series cross-validator. Provides train/test indices to split time series data samples that are observed at fixed time intervals, in train/test sets. It is a variation of k-fold which returns first k folds as train set and the k + 1th fold as test set. Note that unlike standard cross-validation methods, successive training sets are supersets of those that come before them. Also, it adds all surplus data to the first training partition, which is always used to train the model. Key features:

Maintains temporal dependence
Expanding window approach
Forward-chaining splits
No future data leakage

Use cases:

Sequential data
Financial forecasting
Temporal predictions
Time-dependent patterns

Note: Training sets are supersets of previous iterations.

NSplits

u32

Number of temporal splits. Considerations: Typical values:

5: Standard forward chaining
3: Limited historical data
10: Long time series

Impact:

Affects training window growth
Determines validation points
Influences computational load

MaxTrainSize

u64

Maximum size of training set. Should be strictly less than the number of samples. Applications:

0: Use all available past data
>0: Rolling window of fixed size

Use cases:

Limit historical relevance
Control computational cost
Handle concept drift
Memory constraints

TestSize

u64

Number of samples in each test set. When 0:

Auto-calculated as n_samples/(n_splits+1)
Ensures equal-sized test sets

Considerations:

Forecast horizon
Validation requirements
Available future data

Gap

u64

Number of samples to exclude from the end of each train set before the test set.Gap between train and test sets. Uses:

Avoid data leakage
Model forecast lag
Buffer periods

Common scenarios:

0: Continuous prediction
>0: Forward gap for realistic evaluation
Match business forecasting needs