COMP 682 Data Mining (COMP 682)

P(E) is assumed to be the same for all ___ ______ (Naive Bayes) Naive Bayes Pros - Despite strict independence assumptions, performs surprisingly well for classification on real-world tasks - Natural and incremental learner. Needs not reprocess all past training examples when new data arrive - Fast, efficient, and effective quantile(titanic$Age, seq(from=0, to =1, by = .2)) Create a quintile for titanic explaining the Age variable titanic_w1_c50 <- C5.0(Survived~.,titanic) Build a cla...

1 Precision (a or positive) or Positive Predictive Value (PPV) TP/(TP+FP) Positive Predictive Value (PPV) = TP/(TP+FP) 2 Precision (b or negative) or Negative Predictive Value (NPV) TN/(TN+FN) Negative Predictive Value (NPV) = TN/(TN+FN) 3 Recall (a or positive) = True Positive Rate (a) or TPR(a) Sensitivity =TP/(TP+FN) 4 Recall (b or negative) = True Negative Rate (b) Specificity = TN/(TN+FP) 5 F-MEASURE OR F-SCORE 3 items (2 bullet points and the formula) 1...