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Model evaluation is the process of assessing the performance of a machine learning model to determine how well it generalizes to unseen data. This step is crucial for understanding the model's effectiveness and ensuring it meets the desired performance criteria.
EVALUATION METRICES
Confusion Matrix
Confusion Matrix
A confusion matrix provides a visual representation of a model's performance by displaying the true positive, true negative, false positive, and false negative predictions. It helps in understanding where the model is making mistakes.
The ratio of correctly predicted instances to the total instances.
The ratio of correctly predicted instances to the total instances.
The ratio of true positive predictions to the total actual positives, showing how well the model identifies positive instances.
The ratio of true positive predictions to the total actual positives, showing how well the model identifies positive instances.
The ratio of true positive predictions to the total predicted positives, indicating how many of the predicted positive instances were actually positive.
The ratio of true positive predictions to the total predicted positives, indicating how many of the predicted positive instances were actually positive.
The harmonic mean of precision and recall, providing a balance between the two metrics, especially useful for imbalanced datasets. The higher the F1 score, the more accurate your model is in doing predictions.
The harmonic mean of precision and recall, providing a balance between the two metrics, especially useful for imbalanced datasets. The higher the F1 score, the more accurate your model is in doing predictions.
Accuracies for Different Models
Mean Accuracies and Evaluating Key Metrices
Based on the metrics provided, K-Nearest Neighbors (KNN) seems to be the best model overall.
Mean Accuracy: KNN has the highest accuracy at 0.88, indicating that it predicts correctly more often than the other models.
Precision: KNN also has a solid precision of 0.88, meaning that when it predicts a positive class, it’s correct 88% of the time. Although Support Vector Machine (SVM) has the highest precision (0.93), its accuracy is lower (0.78).
Recall: KNN’s recall is 0.88, which suggests it’s doing well at capturing the positive cases. Naive Bayes has a higher recall (0.97), but its lower precision (0.78) means it’s less reliable at avoiding false positives.
F1-score: KNN has the highest F1-score of 0.88, which is the harmonic mean of precision and recall, making it a good balance between the two.
KNN has the best overall balance across all key metrics, with a k value of 5.
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