Bagging / Regressor Layer

A Bagging regressor is an ensemble meta-estimator that fits base regressors each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a meta-estimator can typically be used as a way to reduce the variance of a black-box estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it.

Mathematical formulation: $f_{ba g} (x) = \frac{1}{M} m = 1 \sum M f_{m} (x)$ where:

fₘ are base models
M is ensemble size
Each model trained on bootstrap sample

Key characteristics:

Parallel ensemble
Random resampling
Model averaging
Variance reduction

Advantages:

Reduces overfitting
Handles high variance
Parallel training
Model stability

Common applications:

Noisy data
Unstable models
Variance reduction
Robust prediction

Outputs:

Predicted Table: Results with predictions
Validation Results: Cross-validation metrics
Test Metric: Hold-out performance
Feature Importances: Aggregated importance

Table

Predicted Table

Validation Results

Test Metric

Feature Importances

SelectFeatures

[column, ...]

Feature selection for Bagging:

Requirements:

Data properties:
- Numeric features
- No missing values
- Finite numbers
- Clean data
Preprocessing needs:
- Based on base model
- Handle outliers
- Check correlations
- Scale if needed
Ensemble impact:
- Feature diversity
- Sampling effects
- Memory usage
- Training speed

Note: If empty, uses all numeric columns except target

SelectTarget

column

Target variable for Bagging:

Requirements:

Data type:
- Numeric continuous
- No missing values
- Finite values
- Clean target
Properties:
- Any distribution
- Handle outliers
- Consider scaling
- Check range
Ensemble effects:
- Averaging impact
- Variance reduction
- Prediction stability
- Error patterns

Note: Must be a single numeric column

Params

oneof

DefaultParams

Default Bagging configuration:

Ensemble structure:
- Base: Decision Tree
- Estimators: 10
- Full sample size
Sampling strategy:
- Bootstrap: True
- All features used
- No feature bootstrap
Model behavior:
- No OOB scoring
- Fresh ensemble
- Standard aggregation

Best suited for:

Initial modeling
Quick evaluation
Simple ensembles
Basic variance reduction

CustomParams

Customizable Bagging parameters:

Control aspects:

Ensemble design:
- Base estimator choice
- Number of models
- Aggregation method
Sampling control:
- Sample size
- Feature fraction
- Bootstrap options
Model evaluation:
- OOB estimation
- Warm starting
- Random seeds

Trade-offs:

Diversity vs stability
Memory vs performance
Speed vs accuracy

BaseEstimator

enum

DecisionTree

Base learner selection:

Categories:

Tree-based:
- Hierarchical models
- Non-linear patterns
- Feature interactions
Linear models:
- Linear relationships
- Regularization options
- Fast computation
Other methods:
- Instance-based
- Neural networks
- Robust regression

Selection criteria:

Data complexity
Training speed
Model diversity
Memory usage

DecisionTree ~

Decision Tree base learner:

Strengths:

Handles non-linearity
Captures interactions
No scaling needed
Fast training

Best for:

Standard boosting
Complex patterns
Default choice

RandomForest ~

Random Forest base learner:

Strengths:

Built-in ensemble
Feature randomization
Robust predictions
Low variance

Best for:

Stable boosting
Noisy data
High dimensions

ExtraTrees ~

Extra Trees base learner:

Strengths:

Random splitting
Higher diversity
Fast training
Low variance

Best for:

Quick ensembles
Random patterns
Large datasets

GradientBoosting ~

Gradient Boosting base learner:

Strengths:

Strong predictions
Gradient-based
Sequential learning
High accuracy

Best for:

Complex boosting
Accurate models
Clean data

Knn ~

K-Nearest Neighbors base learner:

Strengths:

Instance-based
Local patterns
Non-parametric
Memory-based

Best for:

Local modeling
Small datasets
Pattern matching

Mlp ~

Multi-Layer Perceptron base learner:

Strengths:

Neural network
Deep patterns
Feature learning
Non-linear mapping

Best for:

Complex functions
Feature extraction
Large datasets

LinearReg ~

Linear Regression base learner:

Strengths:

Simple model
Fast training
Interpretable
Low variance

Best for:

Linear patterns
Quick baselines
Simple boosting

Svr ~

Support Vector Regression base learner:

Strengths:

Kernel methods
Margin-based
Non-linear capacity
Robust models

Best for:

Complex patterns
Small datasets
High precision

LinearSvr ~

Linear SVR base learner:

Strengths:

Fast training
Linear kernel
Scalable method
Memory efficient

Best for:

Large datasets
Linear patterns
Fast boosting

Sgd ~

SGD Regression base learner:

Strengths:

Online learning
Memory efficient
Fast updates
Scalable method

Best for:

Large datasets
Streaming data
Quick training

PassiveAggressive ~

Passive Aggressive base learner:

Strengths:

Online updates
Adaptive learning
Quick adaptation
Margin-based

Best for:

Online learning
Fast updates
Active learning

Ridge ~

Ridge Regression base learner:

Strengths:

L2 regularization
Stable solutions
Feature scaling
Low variance

Best for:

Correlated features
Numerical stability
Regular patterns

Lasso ~

Lasso Regression base learner:

Strengths:

L1 regularization
Feature selection
Sparse solutions
Variable elimination

Best for:

Feature sparsity
High dimensions
Important variables

ElasticNet ~

Elastic Net base learner:

Strengths:

Combined L1/L2
Balanced regularity
Group selection
Stable sparsity

Best for:

Grouped features
Mixed patterns
Robust selection

Huber ~

Huber Regression base learner:

Strengths:

Robust to outliers
Adaptive loss
Combined L1/L2
Stable learning

Best for:

Noisy data
Outlier presence
Robust boosting

Lars ~

Least Angle Regression base learner:

Strengths:

Forward selection
Path algorithms
Efficient compute
Feature ordering

Best for:

Feature analysis
Path computation
Stepwise models

LassoLars ~

Lasso LARS base learner:

Strengths:

L1 with LARS
Path computation
Feature selection
Efficient path

Best for:

Sparse solutions
Path analysis
Feature paths

OrthogonalMatchingPursuit ~

Orthogonal Matching Pursuit learner:

Strengths:

Greedy selection
Forward fitting
Sparse signals
Fast computation

Best for:

Signal processing
Sparse patterns
Quick selection

BayesianRidge ~

Bayesian Ridge base learner:

Strengths:

Probabilistic
Automatic priors
Uncertainty bounds
Adaptive complexity

Best for:

Uncertainty needs
Automatic tuning
Probabilistic boosting

Ardr ~

ARD Regression base learner:

Strengths:

Feature relevance
Automatic scaling
Sparse Bayesian
Adaptive priors

Best for:

Feature selection
Relevance learning
Automatic weighting

NEstimators

u32

Number of base estimators:

Impact on model:

Variance reduction: $Va r (a vg) = \frac{σ ^{2}}{n}$
Ensemble stability
Resource usage

Guidelines:

Small: 10-50 (quick)
Medium: 50-200 (balanced)
Large: 200+ (stable)

Trade-off with resources

MaxSamples

f64

The number of samples to draw from X to train each base estimator.

Calculation: n_samples = max_samples * n_total

Effects:

Smaller: More diversity
Larger: More stability
1.0: Full dataset

Common ranges:

0.5-0.8: High diversity
0.8-1.0: More stable

MaxFeatures

f64

Feature subset fraction:

Calculation: n_features = max_features * n_total

Impact:

Feature diversity
Model complexity
Learning capacity

Typical values:

0.7-1.0: More features
0.4-0.7: More diversity

Bootstrap

bool

true

Whether samples are drawn with replacement.

When True:

With replacement
~63.2% unique samples
Classic bagging

When False:

Without replacement
All unique samples
Subsample ensemble

BootstrapFeatures

bool

false

Whether features are drawn with replacement.

When True:

Random feature sets
More feature diversity
Different views

When False:

Fixed feature subset
Consistent features
Standard approach

OobScore

bool

false

Whether to use out-of-bag samples to estimate the generalization error.

Properties:

Uses unused samples
Unbiased estimation
No extra data needed

Requirements:

bootstrap = True
Sufficient samples
Model support

WarmStart

bool

false

When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble.

When True:

Keep existing models
Add new estimators
Incremental growth

When False:

Fresh ensemble
Complete rebuild
Independent training

RandomState

u64

Random number control:

Influences:

Bootstrap sampling
Feature selection
Base estimators

Usage:

Fixed: Reproducible
0: System random
Different: Variations

GridSearch

Bagging hyperparameter optimization:

Search dimensions:

Ensemble structure:
- Base estimators
- Ensemble size
- Model diversity
Sampling strategies:
- Sample sizes
- Feature fractions
- Bootstrap methods
Evaluation options:
- OOB scoring
- Performance metrics
- Resource usage

Best practices:

Balance diversity
Consider resources
Monitor stability

BaseEstimator

[enum, ...]

DecisionTree

Base learner selection:

Categories:

Tree-based:
- Hierarchical models
- Non-linear patterns
- Feature interactions
Linear models:
- Linear relationships
- Regularization options
- Fast computation
Other methods:
- Instance-based
- Neural networks
- Robust regression

Selection criteria:

Data complexity
Training speed
Model diversity
Memory usage

DecisionTree ~

Decision Tree base learner:

Strengths:

Handles non-linearity
Captures interactions
No scaling needed
Fast training

Best for:

Standard boosting
Complex patterns
Default choice

RandomForest ~

Random Forest base learner:

Strengths:

Built-in ensemble
Feature randomization
Robust predictions
Low variance

Best for:

Stable boosting
Noisy data
High dimensions

ExtraTrees ~

Extra Trees base learner:

Strengths:

Random splitting
Higher diversity
Fast training
Low variance

Best for:

Quick ensembles
Random patterns
Large datasets

GradientBoosting ~

Gradient Boosting base learner:

Strengths:

Strong predictions
Gradient-based
Sequential learning
High accuracy

Best for:

Complex boosting
Accurate models
Clean data

Knn ~

K-Nearest Neighbors base learner:

Strengths:

Instance-based
Local patterns
Non-parametric
Memory-based

Best for:

Local modeling
Small datasets
Pattern matching

Mlp ~

Multi-Layer Perceptron base learner:

Strengths:

Neural network
Deep patterns
Feature learning
Non-linear mapping

Best for:

Complex functions
Feature extraction
Large datasets

LinearReg ~

Linear Regression base learner:

Strengths:

Simple model
Fast training
Interpretable
Low variance

Best for:

Linear patterns
Quick baselines
Simple boosting

Svr ~

Support Vector Regression base learner:

Strengths:

Kernel methods
Margin-based
Non-linear capacity
Robust models

Best for:

Complex patterns
Small datasets
High precision

LinearSvr ~

Linear SVR base learner:

Strengths:

Fast training
Linear kernel
Scalable method
Memory efficient

Best for:

Large datasets
Linear patterns
Fast boosting

Sgd ~

SGD Regression base learner:

Strengths:

Online learning
Memory efficient
Fast updates
Scalable method

Best for:

Large datasets
Streaming data
Quick training

PassiveAggressive ~

Passive Aggressive base learner:

Strengths:

Online updates
Adaptive learning
Quick adaptation
Margin-based

Best for:

Online learning
Fast updates
Active learning

Ridge ~

Ridge Regression base learner:

Strengths:

L2 regularization
Stable solutions
Feature scaling
Low variance

Best for:

Correlated features
Numerical stability
Regular patterns

Lasso ~

Lasso Regression base learner:

Strengths:

L1 regularization
Feature selection
Sparse solutions
Variable elimination

Best for:

Feature sparsity
High dimensions
Important variables

ElasticNet ~

Elastic Net base learner:

Strengths:

Combined L1/L2
Balanced regularity
Group selection
Stable sparsity

Best for:

Grouped features
Mixed patterns
Robust selection

Huber ~

Huber Regression base learner:

Strengths:

Robust to outliers
Adaptive loss
Combined L1/L2
Stable learning

Best for:

Noisy data
Outlier presence
Robust boosting

Lars ~

Least Angle Regression base learner:

Strengths:

Forward selection
Path algorithms
Efficient compute
Feature ordering

Best for:

Feature analysis
Path computation
Stepwise models

LassoLars ~

Lasso LARS base learner:

Strengths:

L1 with LARS
Path computation
Feature selection
Efficient path

Best for:

Sparse solutions
Path analysis
Feature paths

OrthogonalMatchingPursuit ~

Orthogonal Matching Pursuit learner:

Strengths:

Greedy selection
Forward fitting
Sparse signals
Fast computation

Best for:

Signal processing
Sparse patterns
Quick selection

BayesianRidge ~

Bayesian Ridge base learner:

Strengths:

Probabilistic
Automatic priors
Uncertainty bounds
Adaptive complexity

Best for:

Uncertainty needs
Automatic tuning
Probabilistic boosting

Ardr ~

ARD Regression base learner:

Strengths:

Feature relevance
Automatic scaling
Sparse Bayesian
Adaptive priors

Best for:

Feature selection
Relevance learning
Automatic weighting

NEstimators

[u32, ...]

Ensemble size search:

Search patterns:

Basic range:
- [10, 20, 50]
- Quick testing
- Initial study
Full range:
- [50, 100, 200]
- Production use
- Thorough search
Resource-aware:
- Based on compute
- Memory limits
- Time constraints

MaxSamples

[f64, ...]

Sample size search:

Search ranges:

High stability:
- [0.8, 0.9, 1.0]
- More data used
- Less diversity
High diversity:
- [0.5, 0.6, 0.7]
- Different subsets
- More variation
Balance study:
- [0.6, 0.8, 1.0]
- Trade-off analysis

MaxFeatures

[f64, ...]

Feature fraction search:

Search spaces:

Conservative:
- [0.8, 0.9, 1.0]
- Most features
- Stable models
Aggressive:
- [0.4, 0.6, 0.8]
- Feature diversity
- Different views
Mixed approach:
- [0.6, 0.8, 1.0]
- Balance search

Bootstrap

[bool, ...]

true

Bootstrap method search:

Options:

Single mode:
- [true]: Classic bagging
Compare both:
- [true, false]
- Method impact
- Sampling effect

BootstrapFeatures

[bool, ...]

false

Feature bootstrap search:

Options:

Standard: [false]
- Fixed features
- Common approach
Advanced: [true]
- Random features
- More diversity
Compare: [true, false]
- Feature impact study

OobScore

[bool, ...]

false

OOB evaluation search:

Options:

Without: [false]
- Faster training
- No extra metrics
With: [true]
- Built-in validation
- Unbiased estimates

Note: Requires bootstrap=true

WarmStart

[bool, ...]

false

When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble.

Options:

Fresh: [false]
- New ensembles
- Independent runs
Reuse: [true]
- Incremental growth
- Model extension
Compare: [true, false]
- Training impact

RandomState

u64

Random number control:

Influences:

Bootstrap sampling
Feature selection
Base estimators

Usage:

Fixed: Reproducible
0: System random
Different: Variations

RefitScore

enum

R2score

Regression model evaluation metrics:

Purpose:

Model performance evaluation
Error measurement
Quality assessment
Model comparison

Selection criteria:

Error distribution
Scale sensitivity
Domain requirements
Business objectives

Default ~

Model's native scoring method:

Typically R² score
Model-specific implementation
Standard evaluation
Quick assessment

R2score ~

Coefficient of determination (R²):

Formula: $R^{2} = 1 - \frac{\sum ( y _{i} - y ^ _{i} ) ^{2}}{\sum ( y _{i} - y ˉ ) ^{2}}$

Properties:

Range: (-∞, 1]
1: Perfect prediction
0: Constant model
Negative: Worse than mean

Best for:

General performance
Variance explanation
Model comparison
Standard reporting

ExplainedVariance ~

Explained variance score:

Formula: $1 - \frac{Va r ( y - y ^ )}{Va r ( y )}$

Properties:

Range: (-∞, 1]
Accounts for bias
Variance focus
Similar to R²

Best for:

Variance analysis
Bias assessment
Model stability

MaxError ~

Maximum absolute error:

Formula: $max (∣ y_{i} - \overset{y}{^}_{i} ∣)$

Properties:

Worst case error
Original scale
Sensitive to outliers
Upper error bound

Best for:

Critical applications
Error bounds
Safety margins
Risk assessment

NegMeanAbsoluteError ~

Negative mean absolute error:

Formula: $- \frac{1}{n} \sum ∣ y_{i} - \overset{y}{^}_{i} ∣$

Properties:

Linear error scale
Robust to outliers
Original units
Negated for optimization

Best for:

Robust evaluation
Interpretable errors
Outlier presence

NegMeanSquaredError ~

Negative mean squared error:

Formula: $- \frac{1}{n} \sum (y_{i} - \overset{y}{^}_{i})^{2}$

Properties:

Squared error scale
Outlier sensitive
Squared units
Negated for optimization

Best for:

Standard optimization
Large error penalty
Statistical analysis

NegRootMeanSquaredError ~

Negative root mean squared error:

Formula: $- \frac{1}{n} \sum (y_{i} - \overset{y}{^}_{i})^{2}$

Properties:

Original scale
Outlier sensitive
Interpretable units
Negated for optimization

Best for:

Standard reporting
Interpretable errors
Model comparison

NegMeanSquaredLogError ~

Negative mean squared logarithmic error:

Formula: $- \frac{1}{n} \sum (lo g (1 + y_{i}) - lo g (1 + \overset{y}{^}_{i}))^{2}$

Properties:

Relative error scale
For positive values
Sensitive to ratios
Negated for optimization

Best for:

Exponential growth
Relative differences
Positive predictions

NegMedianAbsoluteError ~

Negative median absolute error:

Formula: $- m e d ian (∣ y_{i} - \overset{y}{^}_{i} ∣)$

Properties:

Highly robust
Original scale
Outlier resistant
Negated for optimization

Best for:

Robust evaluation
Heavy-tailed errors
Outlier presence

NegMeanPoissonDeviance ~

Negative Poisson deviance:

Formula: $- 2 \sum (y_{i} lo g (\frac{y _{i}}{y ^ _{i}}) - (y_{i} - \overset{y}{^}_{i}))$

Properties:

For count data
Non-negative values
Poisson assumption
Negated for optimization

Best for:

Count prediction
Event frequency
Rate modeling

NegMeanGammaDeviance ~

Negative Gamma deviance:

Formula: $- 2 \sum (lo g (\frac{y _{i}}{y ^ _{i}}) + \frac{y _{i} - y ^ _{i}}{y ^ _{i}})$

Properties:

For positive continuous data
Constant CV assumption
Relative errors
Negated for optimization

Best for:

Positive continuous data
Multiplicative errors
Financial modeling

NegMeanAbsolutePercentageError ~

Negative mean absolute percentage error:

Formula: $- \frac{100}{n} \sum ∣ \frac{y _{i} - y ^ _{i}}{y _{i}} ∣$

Properties:

Percentage scale
Scale independent
For non-zero targets
Negated for optimization

Best for:

Relative performance
Scale-free comparison
Business metrics

D2AbsoluteErrorScore ~

D² score with absolute error:

Formula: $1 - \frac{\sum ∣ y _{i} - y ^ _{i} ∣}{\sum ∣ y _{i} - y ˉ ∣}$

Properties:

Range: (-∞, 1]
Robust version of R²
Linear error scale
Outlier resistant

Best for:

Robust evaluation
Non-normal errors
Alternative to R²

D2PinballScore ~

D² score with pinball loss:

Properties:

Quantile focus
Asymmetric errors
Risk assessment
Distribution modeling

Best for:

Quantile regression
Risk analysis
Asymmetric costs
Distribution tails

D2TweedieScore ~

D² score with Tweedie deviance:

Properties:

Compound Poisson-Gamma
Flexible dispersion
Mixed distributions
Insurance modeling

Best for:

Insurance claims
Mixed continuous-discrete
Compound distributions
Specialized modeling

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.

TimeSeriesSplitCv

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