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Week 1 / Day 5: Deep Result Analysis and Model Insights

Learning Objectives

Today's focus was on conducting deep result analysis and generating comprehensive insights from our wine classification model. The session involved detailed performance evaluation, feature importance analysis, model confidence assessment, and preparation for the final report of Week 1.

Technical Implementation

Data and Model Loading

The session began by loading the preprocessed wine dataset and the optimized logistic regression model from Day 4:

# Load preprocessed data
data = pd.read_csv('wine_data_cleaned.csv')
X = data[original_features].values  # 13 wine features
y = data['target'].values

# Load best model from Day 4
with open('best_logistic_regression_model.pkl', 'rb') as f:
    best_model = pickle.load(f)

Dataset Characteristics:

  • Features: 13 wine chemical properties
  • Samples: 178 wine samples
  • Classes: 3 wine types (Class 0, 1, 2)
  • Model: Optimized logistic regression with 13 input features

Model Performance Analysis

We conducted comprehensive performance evaluation using the test dataset:

Overall Performance Metrics

  • Training Accuracy: 98.59%
  • Test Accuracy: 100.00%
  • Final Training Loss: 0.1474

Detailed Classification Report

              precision    recall  f1-score   support

     Class 0       1.00      1.00      1.00        12
     Class 1       1.00      1.00      1.00        14
     Class 2       1.00      1.00      1.00        10

    accuracy                           1.00        36
   macro avg       1.00      1.00      1.00        36
weighted avg       1.00      1.00      1.00        36

The model achieved perfect classification on all three wine classes, demonstrating exceptional performance on the test set.

Feature Importance Analysis

We analyzed feature importance across all three classes to understand which chemical properties most influence wine classification:

Class-Specific Feature Importance

Class 0 (Top 5 Features):

  1. Proline: 0.5958 (highest importance)
  2. Alcohol: 0.4982
  3. Alcalinity of Ash: -0.4374
  4. Flavanoids: 0.4031
  5. OD280/OD315: 0.3460

Class 1 (Top 5 Features):

  1. Color Intensity: -0.6632 (highest importance)
  2. Alcohol: -0.6268
  3. Proline: -0.5954
  4. Ash: -0.3984
  5. Hue: 0.3143

Class 2 (Top 5 Features):

  1. Color Intensity: 0.5111 (highest importance)
  2. OD280/OD315: -0.4174
  3. Flavanoids: -0.4145
  4. Hue: -0.4071
  5. Malic Acid: 0.2652

Overall Feature Importance Ranking

  1. Color Intensity: 0.4407
  2. Alcohol: 0.4206
  3. Proline: 0.4056
  4. Alcalinity of Ash: 0.2905
  5. Hue: 0.2748
  6. OD280/OD315: 0.2743
  7. Flavanoids: 0.2740
  8. Ash: 0.2707
  9. Malic Acid: 0.2010
  10. Total Phenols: 0.1920

Model Confidence Analysis

We conducted detailed analysis of model prediction confidence to understand reliability:

Confidence Statistics

  • Average Confidence: 89.64%
  • Confidence Range: 60.41% - 99.93%
  • Confidence for Correct Predictions: 89.64%

Confidence Distribution Analysis

  • Confidence 0.7-0.8: Accuracy 100.0%, Samples 4
  • Confidence 0.8-0.9: Accuracy 100.0%, Samples 5
  • Confidence 0.9-1.0: Accuracy 100.0%, Samples 4
  • Confidence 0.9-1.1: Accuracy 100.0%, Samples 23

Class-Specific Confidence Analysis

Class 0 Predictions:

  • Average predicted probability: 95.46%
  • Range: 67.07% - 99.93%
  • Standard deviation: 8.73%

Class 1 Predictions:

  • Average predicted probability: 84.99%
  • Range: 60.41% - 98.75%
  • Standard deviation: 11.47%

Class 2 Predictions:

  • Average predicted probability: 89.17%
  • Range: 63.07% - 99.16%
  • Standard deviation: 13.64%

Learning Outcomes

Technical Skills Developed

  • Deep Model Analysis: Comprehensive evaluation of model performance across multiple metrics
  • Feature Importance Analysis: Understanding which features drive classification decisions
  • Confidence Assessment: Analyzing prediction reliability and model trustworthiness
  • Visualization Techniques: Creating informative plots for result interpretation
  • Statistical Analysis: Interpreting classification reports and confusion matrices

Key Insights Gained

  • Model Performance: The logistic regression model achieved perfect test accuracy (100%)
  • Feature Dominance: Color Intensity, Alcohol, and Proline are the most influential features
  • Class Differentiation: Each wine class has distinct feature importance patterns
  • Confidence Reliability: High confidence predictions (90%+) show 100% accuracy
  • Model Robustness: Consistent performance across different confidence levels

Analytical Understanding

  • Feature Relationships: Understanding how chemical properties interact to classify wines
  • Model Interpretability: Clear insights into decision-making processes
  • Performance Validation: Comprehensive evaluation confirming model effectiveness
  • Deployment Readiness: Model shows production-ready performance characteristics

Code Repository

All analysis code is saved in the ai-sprint project directory as 05_result_analysis.ipynb, including:

  • Comprehensive model performance evaluation
  • Feature importance analysis with visualizations
  • Model confidence analysis and distribution plots
  • Statistical analysis and reporting functions
  • Data validation and debugging procedures

Week 1 Summary

Completed Learning Objectives

  1. Day 1: Environment setup, NumPy fundamentals, data loading and exploration
  2. Day 2: Pandas data cleaning, visualization, and preprocessing
  3. Day 3: Logistic regression implementation from scratch with regularization
  4. Day 4: Hyperparameter optimization and cross-validation
  5. Day 5: Deep result analysis and comprehensive model evaluation

Final Model Performance

  • Overall Accuracy: 100% on test set
  • Model Complexity: 13 features, 3 classes
  • Training Efficiency: 98.59% training accuracy
  • Generalization: Perfect test performance indicating no overfitting
  • Confidence: High average confidence (89.64%) with reliable predictions

Technical Achievements

  • Implemented logistic regression from scratch
  • Applied advanced regularization techniques
  • Conducted systematic hyperparameter optimization
  • Performed comprehensive cross-validation
  • Generated detailed feature importance analysis
  • Achieved production-ready model performance

Next Steps

Week 2 will focus on implementing additional machine learning algorithms (SVM, Random Forest, Neural Networks) and building a comprehensive model comparison framework to evaluate different approaches for the wine classification task.