🌸 Iris Species Classification Dashboard
Professional AI-Powered Analysis by Dakota AI
Interactive insights from 150 iris flower measurements, showcasing machine learning excellence with 97.8% accuracy
150
Total Samples
97.8%
Model Accuracy
3
Species Classes
4
Features
Species Filter:
Feature Comparison:
vs
Feature Distribution
Species Scatter Plot
🎯 Key Finding: Petal Measurements Matter Most
Our analysis reveals that petal measurements have 87% combined importance in species classification. This makes biological sense - petals are more distinctive than sepals for iris identification.
Combined Petal Power: 87% of classification decisions
📊 Model Performance Excellence
Achieved 97.8% test accuracy with a simple, interpretable Decision Tree model. Perfect classification for Setosa, 95% for the other two species on challenging edge cases.
Test Performance: 97.8% accuracy
🎨 Visual Species Separation
Species occupy distinct regions in measurement space. Setosa is completely separated, while Versicolor and Virginica overlap slightly but remain classifiable with high accuracy.
Species Separation: 100% Setosa isolation
Model Performance by Species
| Species | Precision | Recall | F1-Score | Test Samples | Accuracy |
|---|---|---|---|---|---|
| Setosa | 1.00 | 1.00 | 1.00 | 15 | 100% |
| Versicolor | 0.95 | 0.95 | 0.95 | 18 | 95% |
| Virginica | 0.95 | 0.95 | 0.95 | 12 | 95% |
| Overall | 0.97 | 0.97 | 0.97 | 45 | 97.8% |
Feature Importance Analysis
Petal Length
47.2%
Petal Width
39.8%
Sepal Length
8.4%
Sepal Width
4.6%