Question

1-4. The table below shows the predictions made for a categorical target feature by a model for a test dataset. ID Target Prediction ID Target Prediction ID Target Prediction 8 true 1 false false 15 false false true 2 false 9 false false 16 false false false 3 false false 10 false false 17 true false 4 false false 11 false false 18 true true true False2 7 true 19 true true true true 6 false false 13 false false 20 true true 14 true true true Based on this test set, calculate the misclassification rate. Measure Formula accuracy, recognition rate FP+ FN error rate, misclassification rate sensitivity, true positive rate, recall TN specificity, true negative rate I P precision F, Fi, F-score, harmonic mean of precision and recall 2x precision x recall precision +recall (1 +82) x precision x recall 2xprecisint recall Fß, where β is a non-negative real number Evaluation measures. Note that some measures are known by more than one name. TP,TN, FP, P, N refer to the number of true positive, true negative, false positive, positive, and negative samples, respectively

Based on the test set above, calculate the precision rate.

Based on the test set above, calculate the recall rate.

calculate the F₁ measure.

calculate the misclassification rate.

Answer 1

Tot Precision kıall it-88よど 888 d +소