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Week 1 / Day 4: Model Optimization and Hyperparameter Tuning

Learning Objectives

Today's focus was on model optimization and hyperparameter tuning, implementing systematic approaches to improve model performance through learning rate optimization, regularization analysis, cross-validation techniques, and model deployment preparation.

Technical Implementation

Data Preparation and Model Setup

The session began by loading the preprocessed wine dataset from Day 2 and recreating the LogisticRegression class from Day 3. The dataset was properly split into training (142 samples) and testing (36 samples) sets with stratification to maintain class balance across the three wine types.

# Load cleaned data and separate features/target
df = pd.read_csv('wine_data_cleaned.csv')
X = df.drop(['target', 'wine_type'], axis=1)
y = df['target']

# Standardize features and split data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(
    X_scaled, y, test_size=0.2, random_state=42, stratify=y
)

Learning Rate Optimization

We systematically tested different learning rates to find the optimal value for model convergence:

Learning Rate Analysis Results

  • 0.001: Training Accuracy 95.77%, Test Accuracy 97.22%, Final Loss 0.4117
  • 0.01: Training Accuracy 99.30%, Test Accuracy 100.00%, Final Loss 0.1474
  • 0.1: Training Accuracy 100.00%, Test Accuracy 100.00%, Final Loss 0.1275
  • 0.5: Training Accuracy 100.00%, Test Accuracy 97.22%, Final Loss 0.1272
  • 1.0: Training Accuracy 100.00%, Test Accuracy 97.22%, Final Loss 0.1272

Best Learning Rate: 0.01 achieved perfect test accuracy (100%) with stable convergence.

Regularization Strength Optimization

We evaluated different regularization strengths to prevent overfitting while maintaining performance:

Regularization Analysis Results

  • 0.0: Training 99.30%, Test 100.00%, Weights Norm 2.1691
  • 0.001: Training 99.30%, Test 100.00%, Weights Norm 2.1501
  • 0.01: Training 99.30%, Test 100.00%, Weights Norm 1.9950
  • 0.1: Training 98.59%, Test 100.00%, Weights Norm 1.2483
  • 0.5: Training 98.59%, Test 100.00%, Weights Norm 0.6286
  • 1.0: Training 97.89%, Test 91.67%, Weights Norm 0.4185

Best Regularization Strength: 0.0 (no regularization) achieved optimal performance, indicating the model was not overfitting on this dataset.

L1 vs L2 Regularization Comparison

We conducted a detailed comparison between L1 and L2 regularization techniques:

Regularization Type Analysis

  • L1 Regularization: Test Accuracy 100.00%, Final Loss 0.2040, Weights Norm 1.9467
  • L2 Regularization: Test Accuracy 100.00%, Final Loss 0.1474, Weights Norm 1.9950

Both regularization types achieved perfect test accuracy, but L2 showed slightly lower final loss. The weight distributions were visualized to demonstrate the different regularization effects on model parameters.

K-Fold Cross-Validation

We implemented comprehensive cross-validation to assess model robustness across different data splits:

Cross-Validation Results

  • 3-Fold CV: Mean Accuracy 97.75% ± 0.81%, Range 96.61% - 98.33%
  • 5-Fold CV: Mean Accuracy 97.76% ± 2.08%, Range 94.44% - 100.00%
  • 10-Fold CV: Mean Accuracy 97.78% ± 2.72%, Range 94.44% - 100.00%

Best K-Fold Value: 10-fold provided the highest mean accuracy (97.78%) while maintaining reasonable standard deviation.

Model Deployment Preparation

We implemented multiple model saving and loading methods for production deployment:

Model Persistence Methods

  1. Pickle Serialization: best_logistic_regression_model.pkl
  2. Joblib Serialization: best_logistic_regression_model.joblib
  3. Parameter Export: model_parameters.npz

All loading methods were verified to produce identical predictions, ensuring deployment reliability.

Final Model Performance

  • Training Accuracy: 99.30%
  • Test Accuracy: 100.00%
  • Cross-Validation Accuracy: 97.78%
  • Final Loss: 0.1474

Learning Outcomes

Technical Skills Developed

  • Hyperparameter Optimization: Systematic learning rate and regularization strength tuning
  • Regularization Analysis: Understanding L1 vs L2 effects on model performance
  • Cross-Validation: Implementing k-fold validation for robust model evaluation
  • Model Persistence: Multiple serialization methods for production deployment
  • Performance Analysis: Comprehensive evaluation across multiple metrics

Optimization Insights

  • Learning Rate Impact: Lower learning rates (0.01) provided stable convergence without overshooting
  • Regularization Effects: L2 regularization was more effective than L1 for this dataset
  • Overfitting Prevention: The model showed no signs of overfitting, allowing optimal performance without regularization
  • Cross-Validation Stability: 10-fold CV provided the most reliable performance estimates

Best Practices Learned

  • Systematic Tuning: Methodical approach to hyperparameter optimization
  • Performance Tracking: Monitoring multiple metrics during optimization
  • Model Validation: Using cross-validation to ensure robust performance estimates
  • Deployment Readiness: Multiple persistence methods for production reliability
  • Reproducibility: Consistent random seeds and parameter tracking

Code Repository

All implementation code is saved in the ai-sprint project directory as 04_model_optimization_hyperparameter_tuning.ipynb, including:

  • Learning rate optimization pipeline
  • Regularization strength analysis
  • L1 vs L2 comparison with visualization
  • K-fold cross-validation implementation
  • Model persistence and loading methods
  • Production-ready prediction function

Next Steps

Tomorrow's focus will be on implementing additional machine learning algorithms (SVM, Random Forest) and comparing their performance with our optimized logistic regression implementation, building a comprehensive model comparison framework.