Train Classifier (AdaBoost) Node

AI/ML/Classification

Fit/Train an AdaBoost classifier using multi-class SAMME boosting over shallow Decision Trees. Each learner focuses on the rows its predecessors got wrong, so boosting usually beats a single tree on weak signal, but it is far more sensitive to label noise and outliers than Random Forest. Estimators is a maximum, not a guarantee: boosting stops early once a learner is no better than random guessing.

fit_adaboostml

Inputs6

Outputs3

Security exposure6/10

Packageml

Ratings

Scores range from 0 to 10. Higher values mean more impact, exposure, or operational weight.

SecurityAttack surface and exposure impact.

6/10Medium

PrivacyPotential sensitivity of processed data.

6/10Medium

PerformanceRuntime or resource pressure.

5/10Medium

GovernancePolicy, audit, or compliance impact.

5/10Medium

ReliabilityOperational stability considerations.

6/10Medium

CostExternal or compute cost impact.

5/10Medium

Input Pins

Input

Execution

exec_in

Execution trigger that begins AdaBoost training

Data Source

String

source

Choose which backend supplies the training data

Default Database

Database

Estimators

Integer

n_estimators

Maximum number of boosting rounds. Boosting stops early once a learner performs no better than random guessing, so the fitted model may hold fewer estimators than requested.

Default 50

Range 1 to 2000

Learning Rate

Float

learning_rate

Shrinkage applied to each learner's vote. Must be positive. Values below 1 regularize the ensemble but need more estimators; 0.1 with 500 estimators is a common pairing.

Default 1

Range 0.001 to 2

Base Tree Depth

Integer

max_depth

Depth of each weak learner. AdaBoost is designed around shallow trees; 1 gives classic decision stumps. Deep base trees defeat the point of boosting and overfit quickly.

Default 1

Range 1 to 32

Seed

Integer

seed

Seed for the base learner sampling. Fixing it makes the sampling reproducible. Note that the base trees are not bit-exact across processes: linfa resolves modal-class ties in hash-map iteration order, which Rust re-randomizes on every run.

Default 42

Range 0 to 4294967295

Output Pins

Done

Execution

exec_out

Activated once training completes

Model

Struct

model

Thread-safe handle to the trained AdaBoost classifier

Schema enforced

Estimators Kept

Integer

estimators_kept

Number of estimators actually retained after early stopping, which may be lower than the requested maximum

Node Info

Internal name: fit_adaboost
Category: AI/ML/Classification