most frequent class[[ ]dummy model[[ ]decision tree[[ ]logistic regression [[ ]looking at the confusion matrixit is quite clear that something is wrong with pred_most_frequentbecause it always predicts the same class pred_dummyon the other handhas very small number of true positives ( )particularly compared to the number of false negatives and false positives--there are many more false positives than true positivesthe predictions made by the decision tree make much more sense than the dummy predictionseven though the accuracy was nearly the same finallywe can see that logistic regression does better than pred_tree in all aspectsit has more true positives and true negatives while having fewer false positives and false negatives from this comparisonit is clear that only the decision tree and the logistic regression give reasonable resultsand that the logistic regression works better than the tree on all accounts howeverinspecting the full confusion matrix is bit cumbersomeand while we gained lot of insight from looking at all aspects of the matrixthe process was very manual and qualitative there are several ways to summarize the information in the confusion matrixwhich we will discuss next relation to accuracy we already saw one way to summarize the result in the confusion matrix--by computing accuracywhich can be expressed asaccuracy tp+tn tp+tn fp fn in other wordsaccuracy is the number of correct predictions (tp and tndivided by the number of all samples (all entries of the confusion matrix summed upprecisionrecalland -score there are several other ways to summarize the confusion matrixwith the most common ones being precision and recall precision measures how many of the samples predicted as positive are actually positive model evaluation and improvement