Model Evaluation is Incomplete and Potentially Misleading

[Manu95021]

The current implementation focuses on training the model for Iris classification but lacks proper evaluation metrics, which can lead to misleading conclusions about model performance.

Problems Identified:
The model evaluation is limited or missing (no accuracy, precision, recall, F1-score).
No confusion matrix is used to analyze classification performance.
No train-test split validation explanation (risk of overfitting).
Results are not reproducible due to lack of random_state control.
Suggested Improvements:
Add train_test_split with a fixed random_state.
Include evaluation metrics such as accuracy_score, classification_report.
Visualize results using a confusion matrix.
Optionally perform cross-validation for better reliability.

This will ensure the model performance is properly validated and trustworthy.

[Manu95021]

Heyy@Apaulgithub,
can you please assign it to me