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.
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.