Page 342 - AI Ver 3.0 class 10_Flipbook
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You will see a table showing the distribution of correct and incorrect predictions for each class, typically
as follows:
• True Positives (TP): Correctly predicted instances of the positive class.
• False Positives (FP): Incorrectly predicted instances of the positive class.
• False Negatives (FN): Incorrectly predicted instances of the negative class.
• True Negatives (TN): Correctly predicted instances of the negative class.
The confusion matrix will first show the results for the Logistic Regression algorithm.
You will see the counts of correct and incorrect predictions (TP, TN, FP, FN) for both classes (Bleached
and Unbleached).
Similarly, the confusion matrix will show the results for the SVM (Support Vector Machine)
algorithm.
This will allow you to compare how SVM performs against the other two models in terms of
classification errors.
Lastly, the confusion matrix will display the results for the Random Forest algorithm.
The matrix will show how well Random Forest performed in correctly and incorrectly predicting the
classes.
340 Touchpad Artificial Intelligence (Ver. 3.0)-X

