ML Fundamentals Skill Skill

ML Fundamentals Skill

Master the building blocks of machine learning: from raw data to trained models.

Quick Start

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier

# 1. Load and split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# 2. Create pipeline
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('classifier', RandomForestClassifier(random_state=42))
])

# 3. Train and evaluate
pipeline.fit(X_train, y_train)
score = pipeline.score(X_test, y_test)
print(f"Accuracy: {score:.4f}")

Key Topics

1. Data Preprocessing

| Step | Purpose | Implementation | |------|---------|----------------| | Missing Values | Handle NaN/None | SimpleImputer(strategy='median') | | Scaling | Normalize ranges | StandardScaler() or MinMaxScaler() | | Encoding | Convert categories | OneHotEncoder() or LabelEncoder() | | Outliers | Remove extremes | IQR method or Z-score |

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer

# Define column types
numeric_features = ['age', 'income', 'score']
categorical_features = ['gender', 'city', 'category']

# Create preprocessor
preprocessor = ColumnTransformer([
    ('num', Pipeline([
        ('imputer', SimpleImputer(strategy='median')),
        ('scaler', StandardScaler())
    ]), numeric_features),
    ('cat', Pipeline([
        ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
        ('encoder', OneHotEncoder(handle_unknown='ignore'))
    ]), categorical_features)
])

2. Feature Engineering

| Technique | Use Case | Example | |-----------|----------|---------| | Polynomial | Non-linear relationships | PolynomialFeatures(degree=2) | | Binning | Discretize continuous | KBinsDiscretizer(n_bins=5) | | Log Transform | Right-skewed data | np.log1p(x) | | Interaction | Feature combinations | x1 * x2 |

3. Model Evaluation

from sklearn.model_selection import cross_val_score
from sklearn.metrics import classification_report

# Cross-validation
cv_scores = cross_val_score(model, X, y, cv=5, scoring='f1_weighted')
print(f"CV F1: {cv_scores.mean():.4f} (+/- {cv_scores.std()*2:.4f})")

# Detailed report
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

4. Cross-Validation Strategies

| Strategy | When to Use | |----------|-------------| | KFold | Standard, balanced data | | StratifiedKFold | Imbalanced classification | | TimeSeriesSplit | Temporal data | | GroupKFold | Grouped samples |

Best Practices

DO

Split data BEFORE any preprocessing
Use pipelines for reproducibility
Stratify splits for classification
Log all preprocessing parameters
Version your feature engineering code

DON'T

Don't fit on test data
Don't ignore data leakage
Don't use accuracy for imbalanced data
Don't hard-code parameters

Exercises

Exercise 1: Basic Pipeline

# TODO: Create a pipeline that:
# 1. Imputes missing values
# 2. Scales features
# 3. Trains a logistic regression

Exercise 2: Cross-Validation

# TODO: Implement 5-fold stratified CV
# and report mean and std of F1 score

Unit Test Template

import pytest
import numpy as np
from sklearn.datasets import make_classification

def test_preprocessing_pipeline():
    """Test preprocessing handles missing values."""
    X, y = make_classification(n_samples=100, n_features=10)
    X[0, 0] = np.nan  # Introduce missing value

    pipeline = create_preprocessing_pipeline()
    X_transformed = pipeline.fit_transform(X)

    assert not np.isnan(X_transformed).any()
    assert X_transformed.shape[0] == X.shape[0]

def test_no_data_leakage():
    """Verify preprocessing doesn't leak test data."""
    X_train, X_test = X[:80], X[80:]

    pipeline.fit(X_train)
    X_test_transformed = pipeline.transform(X_test)

    # Check that test transform uses train statistics
    assert pipeline.named_steps['scaler'].mean_ is not None

Troubleshooting

| Problem | Cause | Solution | |---------|-------|----------| | NaN in prediction | Missing imputer | Add SimpleImputer to pipeline | | Shape mismatch | Inconsistent features | Use ColumnTransformer | | Memory error | Too many one-hot features | Use max_categories or hashing | | Poor CV variance | Data leakage | Check preprocessing order |

Related Resources

Agent: 01-ml-fundamentals
Next Skill: supervised-learning
Docs: scikit-learn User Guide

Version: 1.4.0 | Status: Production Ready

Agent Skills: ML Fundamentals Skill

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