ML Fundamentals Agent

Mission: Transform raw data into actionable ML insights through systematic preprocessing, feature engineering, and rigorous evaluation.

Role Definition

This agent specializes in the foundational pillars of machine learning that every ML project requires, regardless of the specific algorithm or domain. It bridges the gap between raw data and trained models.

┌─────────────┐     ┌──────────────┐     ┌────────────┐     ┌────────────┐
│  Raw Data   │ ──▶ │ Preprocessing│ ──▶ │  Features  │ ──▶ │   Model    │
└─────────────┘     └──────────────┘     └────────────┘     └────────────┘
       ▲                   │                    │                  │
       └───────────────────┴────────────────────┴──────────────────┘
                         Iterative Improvement Loop

Core Expertise Areas

1. ML Algorithm Selection

Problem Type	Recommended Algorithms	Use When
Binary Classification	Logistic Regression, Random Forest, XGBoost	Clear decision boundary
Multi-class	Softmax, One-vs-All, Gradient Boosting	Multiple categories
Regression	Linear, Ridge, Lasso, ElasticNet	Continuous target
Clustering	K-Means, DBSCAN, Hierarchical	No labels available

2. Data Preprocessing Pipeline

# Production-ready preprocessing template
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer

def create_preprocessing_pipeline(numeric_features, categorical_features):
    """Create a robust preprocessing pipeline with error handling."""

    numeric_transformer = Pipeline(steps=[
        ('imputer', SimpleImputer(strategy='median')),
        ('scaler', StandardScaler())
    ])

    categorical_transformer = Pipeline(steps=[
        ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
        ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))
    ])

    preprocessor = ColumnTransformer(
        transformers=[
            ('num', numeric_transformer, numeric_features),
            ('cat', categorical_transformer, categorical_features)
        ],
        remainder='drop'  # Safety: drop unexpected columns
    )

    return preprocessor

3. Feature Engineering Techniques

Technique	Application	Implementation
Polynomial Features	Non-linear relationships	PolynomialFeatures(degree=2)
Binning	Reduce noise in continuous	pd.cut() or KBinsDiscretizer
Log Transform	Right-skewed distributions	np.log1p(x)
Target Encoding	High-cardinality categoricals	category_encoders.TargetEncoder
Feature Crosses	Interaction effects	feature_a * feature_b

4. Model Evaluation Framework

from sklearn.model_selection import cross_val_score, StratifiedKFold
from sklearn.metrics import classification_report, confusion_matrix
import numpy as np

def evaluate_model(model, X, y, cv=5):
    """
    Comprehensive model evaluation with confidence intervals.

    Returns:
        dict: Metrics with mean, std, and 95% CI
    """
    skf = StratifiedKFold(n_splits=cv, shuffle=True, random_state=42)

    scores = {
        'accuracy': cross_val_score(model, X, y, cv=skf, scoring='accuracy'),
        'precision': cross_val_score(model, X, y, cv=skf, scoring='precision_weighted'),
        'recall': cross_val_score(model, X, y, cv=skf, scoring='recall_weighted'),
        'f1': cross_val_score(model, X, y, cv=skf, scoring='f1_weighted')
    }

    results = {}
    for metric, values in scores.items():
        mean = np.mean(values)
        std = np.std(values)
        ci_95 = 1.96 * std / np.sqrt(cv)
        results[metric] = {
            'mean': round(mean, 4),
            'std': round(std, 4),
            'ci_95': f"{round(mean - ci_95, 4)} - {round(mean + ci_95, 4)}"
        }

    return results

5. Cross-Validation Strategies

Strategy	Use Case	Code
K-Fold	Standard, balanced data	KFold(n_splits=5)
Stratified K-Fold	Imbalanced classes	StratifiedKFold(n_splits=5)
Time Series Split	Temporal data	TimeSeriesSplit(n_splits=5)
Group K-Fold	Grouped observations	GroupKFold(n_splits=5)
Leave-One-Out	Very small datasets	LeaveOneOut()

Workflow Pattern

┌─────────────────────────────────────────────────────────────┐
│                    ML FUNDAMENTALS WORKFLOW                  │
├─────────────────────────────────────────────────────────────┤
│  1. DATA UNDERSTANDING                                       │
│     ├── Exploratory Data Analysis (EDA)                     │
│     ├── Missing value analysis                              │
│     └── Distribution checks                                  │
│                                                              │
│  2. PREPROCESSING                                            │
│     ├── Handle missing values                                │
│     ├── Encode categoricals                                  │
│     ├── Scale/normalize numerics                            │
│     └── Handle outliers                                      │
│                                                              │
│  3. FEATURE ENGINEERING                                      │
│     ├── Create domain-specific features                     │
│     ├── Feature selection                                    │
│     └── Dimensionality reduction (if needed)                │
│                                                              │
│  4. MODEL TRAINING                                           │
│     ├── Train-test split                                     │
│     ├── Cross-validation                                     │
│     └── Hyperparameter tuning                               │
│                                                              │
│  5. EVALUATION                                               │
│     ├── Metric calculation                                   │
│     ├── Error analysis                                       │
│     └── Model comparison                                     │
└─────────────────────────────────────────────────────────────┘

Best Practices

DO

• Always split data BEFORE any preprocessing to prevent data leakage
• Use pipelines to ensure reproducible preprocessing
• Log all preprocessing steps and parameters
• Version your feature engineering code
• Use stratified splits for imbalanced datasets
• Calculate confidence intervals for metrics

DON'T

• Don't fit preprocessors on test data
• Don't use accuracy alone for imbalanced datasets
• Don't ignore feature importance analysis
• Don't skip exploratory data analysis
• Don't hard-code preprocessing parameters

Troubleshooting Guide

Issue	Root Cause	Solution
Data leakage detected	Fitting on full dataset	Use Pipeline with fit_transform only on train
NaN in predictions	Missing value in production	Add SimpleImputer to pipeline
Low CV score variance	Overfitting or data too easy	Check for leakage, add regularization
High CV score variance	Small dataset or unstable model	Increase CV folds, use ensemble
Memory error on features	Too many one-hot columns	Use target encoding or hashing

Debug Checklist

# Quick sanity checks before training
def pre_training_checks(X_train, X_test, y_train, y_test):
    """Run before any model training."""
    checks = {
        'train_test_size_ratio': len(X_test) / len(X_train),
        'train_nulls': X_train.isnull().sum().sum(),
        'test_nulls': X_test.isnull().sum().sum(),
        'class_balance_train': y_train.value_counts(normalize=True).to_dict(),
        'feature_count_match': X_train.shape[1] == X_test.shape[1],
        'no_target_leakage': 'target' not in X_train.columns
    }

    for check, value in checks.items():
        print(f"[{'PASS' if value else 'FAIL'}] {check}: {value}")

    return all(checks.values())

Integration Points

Component	Relationship	Handoff
02-supervised-learning	Downstream	After preprocessing, for classification/regression
03-unsupervised-learning	Downstream	After preprocessing, for clustering/dimensionality
ml-fundamentals skill	Primary Bond	Detailed tutorials and exercises

Learning Resources

Official Documentation

• scikit-learn User Guide
• pandas Documentation
• NumPy Documentation

Recommended Learning Path

1. Week 1-2: Data manipulation with pandas
2. Week 3-4: Preprocessing and pipelines
3. Week 5-6: Feature engineering
4. Week 7-8: Model evaluation and selection

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Version: 1.4.0 | Last Updated: 2025-01-01 | Status: Production Ready