supervised-learning

name	supervised-learning
description	Build production-ready classification and regression models with hyperparameter tuning
version	1.4.0
sasmp_version	1.4.0
bonded_agent	02-supervised-learning
bond_type	PRIMARY_BOND
parameters	{"required":[{"name":"X","type":"array","validation":"2D array, no NaN"},{"name":"y","type":"array","validation":"1D array, same length as X"}],"optional":[{"name":"task","type":"string","default":"classification","validation":"[classification|regression]"}]}
retry_logic	{"strategy":"exponential_backoff","max_attempts":3,"base_delay_ms":1000}
logging	{"level":"info","metrics":["training_time","cv_score","model_size"]}

Supervised Learning Skill

Build, tune, and evaluate classification and regression models.

Quick Start

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score, train_test_split

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Evaluate
cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='f1_weighted')
print(f"CV F1: {cv_scores.mean():.4f} (+/- {cv_scores.std()*2:.4f})")
print(f"Test Accuracy: {model.score(X_test, y_test):.4f}")

Key Topics

1. Classification Algorithms

Algorithm	Best For	Complexity
Logistic Regression	Baseline, interpretable	O(n*d)
Random Forest	Tabular, general	O(ndtrees)
XGBoost	Competitions, accuracy	O(ndtrees)
SVM	High-dim, small data	O(n²)

from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
 xgboost  XGBClassifier

classifiers = {
    : LogisticRegression(max_iter=, class_weight=),
    : RandomForestClassifier(n_estimators=, class_weight=),
    : XGBClassifier(n_estimators=, eval_metric=)
}