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.



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