Model Assessment

Accuracy

Value: 88.2%

Meaning: The model correctly predicts 88.2% of all instances.

Assessment: This is a decent overall performance, indicating that the model performs well on the whole dataset. However, accuracy alone can be misleading, especially if the dataset is imbalanced.

Precision

Value: 92.3%

Meaning: Of all the instances predicted as positive (class 1), 92.3% were correct.

Assessment: This high precision suggests that the model is cautious when predicting positive cases and avoids false positives. This could be useful if false positives are more costly in your use case.

Recall

Value: 75%

Meaning: The model correctly identified 75% of the actual positive cases.

Assessment: The recall is lower than precision, indicating that the model misses some true positive cases (false negatives). If false negatives are critical (e.g., in healthcare scenarios like cancer prediction), this needs improvement.

F1 Score

Value: 82.8%

Meaning: The harmonic mean of precision and recall is 82.8%.

Assessment: The F1 score balances precision and recall. While it shows solid performance, the imbalance between precision and recall suggests room for improvement to enhance the model's general effectiveness.

Confusion Matrix

• True Positives (TP): 24
• True Negatives (TN): 51
• False Positives (FP): 2
• False Negatives (FN): 8

Insights:

• False Positives (FP = 2): Only 2 instances were incorrectly predicted as positive. The low FP count aligns with the high precision.
• False Negatives (FN = 8): 8 positive cases were missed by the model, which impacts recall.
• Class Balance: Assuming equal importance of both classes, the model's performance seems relatively balanced. However, false negatives might need attention if class 1 is more critical.

Overall Assessment

Strengths:

• High precision and accuracy indicate that the model makes relatively few incorrect predictions.
• The confusion matrix shows that false positives are minimal, which is beneficial if misclassifying a negative as positive has a significant cost.

Weaknesses:

• The recall is moderate (75%), meaning the model misses some positive cases, which could be problematic depending on the application.
• If the dataset is imbalanced, accuracy and precision might be inflated, and more focus should be given to recall and the F1 score.