AdaBoostDefaultEstimators / Classifier Layer

AdaBoost Classification with Multiple Base Estimator Options:

Mathematical form: $F (x) = sign (m = 1 \sum M α_{m} h_{m} (x))$ where:

$h_{m} (x)$ are different base estimators
$α_{m}$ are estimator weights
M is number of estimators

Key characteristics:

Flexible base estimator choice
Adaptive sample weighting
Sequential ensemble learning
Weighted majority voting
Focus on hard examples

Common applications:

Multi-model ensembles
Complex classification
Hybrid learning systems
Robust predictions

Outputs:

Predicted Table: Original data with predictions
Validation Results: Cross-validation metrics
Test Metric: Hold-out set performance
ROC Curve Data: ROC analysis data
Confusion Matrix: Class prediction breakdown
Feature Importances: Variable importance scores

Table

Predicted Table

Validation Results

Test Metric

ROC Curve Data

Confusion Matrix

Feature Importances

SelectFeatures

[column, ...]

Feature columns for AdaBoost with multiple base estimators:

Data requirements:

General format:
- Numeric features
- Encoded categoricals
- No missing values
- Clean data
Preprocessing needs:
- Varies by base estimator
- Trees: No scaling needed
- Linear models: Scale features
- SVMs: Scale to [-1, 1]
Quality checks:
- Remove constants
- Handle missing data
- Check correlations
- Ensure relevance

Note: If empty, uses all numeric columns except target

SelectTarget

column

Target column for AdaBoost classification:

Requirements:

Data format:
- Categorical labels
- At least two classes
- No missing values
- Clean encoding
Class properties:
- Any distribution
- AdaBoost handles imbalance
- Maintains class order
- Supports multi-class
Quality checks:
- Validate categories
- Check distributions
- Monitor rare classes
- Verify encoding

Params

oneof

DefaultParams

Default configuration for AdaBoost with multiple base estimators:

Ensemble parameters:
- N estimators: 50
- Learning rate: 1.0
- Algorithm: SAMME.R
Base estimator:
- Default: Decision Tree
- Max depth: 1 (decision stump)
- Standard tree parameters

Characteristics:

Balanced complexity
Quick training
Memory efficient
Good baseline

Best suited for:

Initial modeling
Quick prototyping
Baseline benchmarks
Standard datasets

CustomParams

Fine-tuned configuration for AdaBoost with multiple base estimators:

Parameter categories:

Ensemble structure:
- Base estimator choice
- Number of estimators
- Learning rate
Model control:
- Random state
- Reproducibility
- Training stability

Note: Base estimator selection significantly impacts ensemble behavior

BaseEstimator

enum

DecisionTree

Base estimator options for AdaBoost ensemble:

Selection criteria:

Model complexity
Training speed
Memory usage
Prediction time

Note: Each estimator adds its unique characteristics to the ensemble

DecisionTree ~

Decision Tree classifier:

Properties:

Non-linear patterns
Feature interactions
Fast training
Interpretable rules

Best for:

Traditional AdaBoost
Quick training
Standard tasks

RandomForest ~

Random Forest classifier:

Properties:

Double ensemble effect
Built-in feature sampling
Parallel processing
High stability

Best for:

Complex problems
Noisy data
High dimensions

ExtraTrees ~

Extremely Randomized Trees classifier:

Properties:

Random split points
Lower variance
Faster training
More randomization

Best for:

Quick ensemble building
Large datasets
Noise tolerance

GradientBoosting ~

Gradient Boosting classifier:

Properties:

Gradient optimization
Strong predictive power
Sequential learning
Fine-tuned predictions

Best for:

High accuracy needs
Structured data
Competition tasks

Knn ~

K-Nearest Neighbors classifier:

Properties:

Instance-based learning
No training phase
Local patterns
Memory-based

Best for:

Small to medium datasets
Pattern recognition
Simple problems

Mlp ~

Multi-Layer Perceptron classifier:

Properties:

Neural network base
Non-linear mapping
Feature learning
Universal approximation

Best for:

Complex patterns
Feature hierarchies
Deep learning tasks

LogisiticReg ~

Logistic Regression classifier:

Properties:

Linear boundaries
Probability outputs
Fast training
Simple model

Best for:

Linear problems
Probability needs
Interpretability

Svc ~

Support Vector classifier:

Properties:

Kernel methods
Maximum margin
Robust to outliers
Non-linear capability

Best for:

Medium datasets
Complex boundaries
High dimensions

LinearSvc ~

Linear Support Vector classifier:

Properties:

Linear boundaries
Faster than kernel SVC
Large scale capable
Memory efficient

Best for:

Large datasets
High dimensions
Linear problems

GaussianNb ~

Gaussian Naive Bayes classifier:

Properties:

Probabilistic model
Fast training/inference
Feature independence
Continuous features

Best for:

Real-valued data
Quick baseline
Simple problems

BernoulliNb ~

Bernoulli Naive Bayes classifier:

Properties:

Binary/boolean features
Document classification
Fast computation
Probabilistic model

Best for:

Text data
Binary features
Sparse datasets

MultinomialNb ~

Multinomial Naive Bayes classifier:

Properties:

Count-based features
Document classification
Fast processing
Probability outputs

Best for:

Text classification
Discrete features
Count data

Lda ~

Linear Discriminant Analysis classifier:

Properties:

Linear dimensionality reduction
Gaussian assumptions
Fast computation
Stable performance

Best for:

Multi-class problems
Dimensionality reduction
Normal distributions

Qda ~

Quadratic Discriminant Analysis classifier:

Properties:

Quadratic boundaries
Class-specific covariance
More flexible than LDA
Gaussian assumptions

Best for:

Non-linear problems
Different class variances
Normal distributions

Sgd ~

Stochastic Gradient Descent classifier:

Properties:

Online learning
Large scale handling
Multiple loss functions
Linear model

Best for:

Large datasets
Streaming data
Limited memory

PassiveAggressive ~

Passive Aggressive classifier:

Properties:

Online learning
Margin-based approach
Adaptive updates
Fast training

Best for:

Online learning
Stream processing
Quick updates

Ridge ~

Ridge classifier:

Properties:

L2 regularization
Linear boundaries
Stable solutions
Fast computation

Best for:

Linear problems
Correlated features
Numerical stability

NEstimators

u32

Number of boosting stages:

Guidelines:

Small (10-50): Quick models
Medium (50-200): Standard use
Large (>200): Complex problems

Trade-offs:

More estimators: Better performance but slower
Consider learning rate interaction
Watch for convergence

LearningRate

f64

Contribution weight for each classifier:

Typical ranges:

High (0.5-1.0): Fast learning, risk of overfitting
Medium (0.1-0.5): Balanced learning
Low (0.01-0.1): Slower, more robust learning

Note: Inversely related to n_estimators

Random number generation control. Controls the random seed given at each estimator at each boosting iteration. Thus, it is only used when estimator exposes a random_state. Pass an int for reproducible output across multiple function calls.

Affects:

Base estimator randomization
Training reproducibility

Set fixed value for reproducible results

GridSearch

Hyperparameter optimization for AdaBoost with multiple base estimators:

Search process:

Model selection:
- Base estimator types
- Ensemble size
- Learning dynamics
Training control:
- Learning rates
- Sample weights
- Model complexity

Computational impact:

Time: O(n_base_models * n_estimators * n_samples)
Memory: O(n_base_models * n_estimators)

Best practices:

Start with few base models
Test different estimator types
Balance complexity
Monitor resources

BaseEstimator

[enum, ...]

DecisionTree

Base estimator options for AdaBoost ensemble:

Selection criteria:

Model complexity
Training speed
Memory usage
Prediction time

Note: Each estimator adds its unique characteristics to the ensemble

DecisionTree ~

Decision Tree classifier:

Properties:

Non-linear patterns
Feature interactions
Fast training
Interpretable rules

Best for:

Traditional AdaBoost
Quick training
Standard tasks

RandomForest ~

Random Forest classifier:

Properties:

Double ensemble effect
Built-in feature sampling
Parallel processing
High stability

Best for:

Complex problems
Noisy data
High dimensions

ExtraTrees ~

Extremely Randomized Trees classifier:

Properties:

Random split points
Lower variance
Faster training
More randomization

Best for:

Quick ensemble building
Large datasets
Noise tolerance

GradientBoosting ~

Gradient Boosting classifier:

Properties:

Gradient optimization
Strong predictive power
Sequential learning
Fine-tuned predictions

Best for:

High accuracy needs
Structured data
Competition tasks

Knn ~

K-Nearest Neighbors classifier:

Properties:

Instance-based learning
No training phase
Local patterns
Memory-based

Best for:

Small to medium datasets
Pattern recognition
Simple problems

Mlp ~

Multi-Layer Perceptron classifier:

Properties:

Neural network base
Non-linear mapping
Feature learning
Universal approximation

Best for:

Complex patterns
Feature hierarchies
Deep learning tasks

LogisiticReg ~

Logistic Regression classifier:

Properties:

Linear boundaries
Probability outputs
Fast training
Simple model

Best for:

Linear problems
Probability needs
Interpretability

Svc ~

Support Vector classifier:

Properties:

Kernel methods
Maximum margin
Robust to outliers
Non-linear capability

Best for:

Medium datasets
Complex boundaries
High dimensions

LinearSvc ~

Linear Support Vector classifier:

Properties:

Linear boundaries
Faster than kernel SVC
Large scale capable
Memory efficient

Best for:

Large datasets
High dimensions
Linear problems

GaussianNb ~

Gaussian Naive Bayes classifier:

Properties:

Probabilistic model
Fast training/inference
Feature independence
Continuous features

Best for:

Real-valued data
Quick baseline
Simple problems

BernoulliNb ~

Bernoulli Naive Bayes classifier:

Properties:

Binary/boolean features
Document classification
Fast computation
Probabilistic model

Best for:

Text data
Binary features
Sparse datasets

MultinomialNb ~

Multinomial Naive Bayes classifier:

Properties:

Count-based features
Document classification
Fast processing
Probability outputs

Best for:

Text classification
Discrete features
Count data

Lda ~

Linear Discriminant Analysis classifier:

Properties:

Linear dimensionality reduction
Gaussian assumptions
Fast computation
Stable performance

Best for:

Multi-class problems
Dimensionality reduction
Normal distributions

Qda ~

Quadratic Discriminant Analysis classifier:

Properties:

Quadratic boundaries
Class-specific covariance
More flexible than LDA
Gaussian assumptions

Best for:

Non-linear problems
Different class variances
Normal distributions

Sgd ~

Stochastic Gradient Descent classifier:

Properties:

Online learning
Large scale handling
Multiple loss functions
Linear model

Best for:

Large datasets
Streaming data
Limited memory

PassiveAggressive ~

Passive Aggressive classifier:

Properties:

Online learning
Margin-based approach
Adaptive updates
Fast training

Best for:

Online learning
Stream processing
Quick updates

Ridge ~

Ridge classifier:

Properties:

L2 regularization
Linear boundaries
Stable solutions
Fast computation

Best for:

Linear problems
Correlated features
Numerical stability

NEstimators

[u32, ...]

Number of boosting stages to evaluate:

Search patterns:

Quick: [10, 50, 100]
Standard: [50, 100, 200]
Extensive: [100, 250, 500]

Note: Balance with learning_rate values

LearningRate

[f64, ...]

Learning rate values to search:

Common ranges:

Coarse: [0.01, 0.1, 1.0]
Fine: [0.01, 0.05, 0.1, 0.5]
Detailed: [0.001, 0.01, 0.05, 0.1]

Note: Inversely related to n_estimators

RandomState

u64

Random seed for reproducibility:

Controls:

Base estimator randomization
Sample weight initialization
Cross-validation splits

Fixed value ensures reproducible search

RefitScore

enum

Accuracy

Performance evaluation metrics for AdaBoost classification:

Purpose:

Model evaluation
Ensemble selection
Early stopping
Performance tracking

Selection criteria:

Problem objectives
Class distribution
Ensemble size
Computation resources

Default ~

Uses ensemble's built-in scoring:

Properties:

Weighted accuracy metric
Ensemble-aware scoring
Fast computation
Boosting-compatible

Best for:

Standard problems
Quick evaluation
Initial testing
Performance tracking

Accuracy ~

Standard classification accuracy:

Formula: $Accuracy = \frac{Correct Predictions}{Total Samples}$

Properties:

Range: [0, 1]
Ensemble consensus
Intuitive metric
Equal error weights

Best for:

Balanced datasets
Equal error costs
Simple evaluation
Quick benchmarking

BalancedAccuracy ~

Class-normalized accuracy score:

Formula: $\frac{1}{K} \sum_{i = 1}^{K} \frac{T P _{i}}{T P _{i} + F N _{i}}$

Properties:

Range: [0, 1]
Class-weighted
Imbalance-robust
Fair evaluation

Best for:

Imbalanced data
Varied class sizes
Minority focus
Fair assessment

LogLoss ~

Logarithmic loss (Cross-entropy):

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

Properties:

Range: [0, ∞)
Probability-sensitive
Boosting-optimal
Confidence-aware

Best for:

Probability estimation
Boosting optimization
Risk assessment
Model calibration

RocAuc ~

Area Under ROC Curve:

Properties:

Range: [0, 1]
Ranking quality
Threshold-invariant
Ensemble-appropriate

Best for:

Binary problems
Ranking tasks
Score calibration
Model comparison

Note: Extended to multi-class via averaging

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