⚙️ Model Selection and Tuning

Master hyperparameter optimization, algorithm comparison, and systematic model improvement strategies

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Model Selection and Tuning Curriculum

11
Core Units
~65
Key Concepts
12+
Optimization Methods
30+
Practical Examples
1

Model Selection Framework

Understand the systematic approach to choosing the best machine learning algorithm.

  • Model selection process
  • Algorithm comparison strategies
  • Performance vs complexity
  • Domain considerations
  • Data size constraints
  • Interpretability requirements
  • Computational resources
  • Selection criteria framework
2

Hyperparameter Fundamentals

Learn what hyperparameters are and their impact on model performance.

  • Hyperparameter definition
  • Parameters vs hyperparameters
  • Common hyperparameter types
  • Impact on model behavior
  • Hyperparameter spaces
  • Continuous vs discrete
  • Hyperparameter interactions
  • Default value analysis
3

Grid Search

Master exhaustive grid search for systematic hyperparameter optimization.

  • Grid search concept
  • Parameter grid definition
  • Exhaustive search process
  • Cross-validation integration
  • Grid search advantages
  • Computational complexity
  • Grid design strategies
  • Implementation best practices
4

Random Search

Learn efficient random sampling for hyperparameter exploration.

  • Random search methodology
  • Sampling distributions
  • Random vs grid search
  • Efficiency advantages
  • High-dimensional spaces
  • Search budget allocation
  • Convergence properties
  • Practical implementation
5

Bayesian Optimization

Explore intelligent hyperparameter search using Bayesian methods.

  • Bayesian optimization principles
  • Gaussian process models
  • Acquisition functions
  • Exploration vs exploitation
  • Sequential decision making
  • Surrogate model updates
  • Advanced acquisition strategies
  • Implementation frameworks
6

Cross-Validation for Selection

Apply cross-validation techniques for robust model selection and tuning.

  • CV in model selection
  • Nested cross-validation
  • Stratified CV strategies
  • Time series CV
  • Leave-one-out considerations
  • CV fold optimization
  • Variance in CV scores
  • Statistical significance
7

Automated Machine Learning

Understand AutoML approaches for automated model selection and tuning.

  • AutoML overview
  • Neural architecture search
  • Automated feature engineering
  • Pipeline optimization
  • Meta-learning approaches
  • AutoML frameworks
  • Performance vs automation
  • Human-in-the-loop systems
8

Multi-Objective Optimization

Balance multiple objectives like accuracy, speed, and interpretability.

  • Multi-objective formulation
  • Pareto efficiency
  • Trade-off analysis
  • Accuracy vs interpretability
  • Speed vs performance
  • Memory vs accuracy
  • Scalarization methods
  • Decision making frameworks
9

Model Ensemble Selection

Learn to select and combine multiple models for improved performance.

  • Ensemble motivation
  • Diversity importance
  • Base model selection
  • Voting strategies
  • Stacking approaches
  • Blending techniques
  • Dynamic ensemble selection
  • Ensemble pruning
10

Early Stopping and Regularization

Implement stopping criteria and regularization for optimal model complexity.

  • Early stopping mechanisms
  • Validation-based stopping
  • Patience parameters
  • Learning curve analysis
  • Regularization selection
  • L1 vs L2 regularization
  • Dropout strategies
  • Complexity control
11

Production Considerations

Consider real-world constraints in model selection and deployment.

  • Latency requirements
  • Memory constraints
  • Scalability needs
  • Model interpretability
  • Maintenance costs
  • A/B testing frameworks
  • Model versioning
  • Monitoring and updates

Unit 1: Model Selection Framework

Understand the systematic approach to choosing the best machine learning algorithm.

Model Selection Process

Learn the systematic steps for choosing the best machine learning algorithm for your problem.

Process Systematic Methodology
Model selection involves systematically comparing different algorithms, evaluating their performance, and choosing the best one based on multiple criteria including accuracy, interpretability, and computational efficiency.

Algorithm Comparison Strategies

Understand how to fairly compare different machine learning algorithms.

Fair Comparison Requirements:
• Same train/validation/test splits
• Consistent preprocessing
• Proper hyperparameter tuning for each algorithm
• Statistical significance testing
from sklearn.model_selection import cross_val_score, StratifiedKFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import make_classification
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd

def comprehensive_model_comparison():
  """Compare multiple algorithms systematically"""
  
  # Generate sample data
  X, y = make_classification(n_samples=1000, n_features=20,
                               n_informative=15, random_state=42)
  
  # Standardize features (important for some algorithms)
  scaler = StandardScaler()
  X_scaled = scaler.fit_transform(X)
  
  # Define models to compare
  models = {
    'Logistic Regression': LogisticRegression(random_state=42),
    'Random Forest': RandomForestClassifier(random_state=42),
    'SVM': SVC(random_state=42),
    'K-NN': KNeighborsClassifier(),
  }
  
  # Cross-validation setup
  cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
  
  print("=== ALGORITHM COMPARISON ===")
  results = {}
  
  for name, model in models.items():
    # Use scaled data for algorithms that need it
    if name in ['Logistic Regression', 'SVM', 'K-NN']:
      X_input = X_scaled
    else:
      X_input = X
    
    # Perform cross-validation
    scores = cross_val_score(model, X_input, y, cv=cv, scoring='accuracy')
    
    results[name] = {
      'mean': scores.mean(),
      'std': scores.std(),
      'scores': scores
    }
    
    print(f"{name:20s}: {scores.mean():.3f} (+/- {scores.std()*2:.3f})")
  
  # Rank models by performance
  print("\\n=== MODEL RANKING ===")
  ranked = sorted(results.items(), key=lambda x: x[1]['mean'], reverse=True)
  
  for i, (name, result) in enumerate(ranked, 1):
    print(f"{i}. {name}: {result['mean']:.3f}")
  
  return results

comprehensive_model_comparison()

print("\\n=== COMPARISON CONSIDERATIONS ===")
print("✅ Same data splits for fair comparison")
print("✅ Appropriate preprocessing for each algorithm")
print("✅ Cross-validation for robust estimates")
print("✅ Statistical significance testing needed")
print("⚠️ Consider: speed, interpretability, memory usage")