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y P(y|x) y P(y|x) 0 0.1 0 0.55 0 0.1 1 0.7 0 0.25 1 0.8 1 0.25 0 0.85 0 0.3 1 0.9 0 0.33 1 0.9 1 0.4 1 0.95 0 0.52 1 1.0 Beyond just calculating accuracy, we discussed recall and precision as two other measure of a classifier's abilities. Remember that we defined recall and precision as in terms of true positives, false positives, true negatives, and false negatives: Recall = #TruePositives / (#TruePositives + #FalseNegatives) and Precision = #TruePositives / (#TruePositives + #FalsePositives) Q2 Computing Recall and Precision [3pts]. To get a feeling for recall and precision, consider the set of true labels (y) and model predictions P(y|x) shown in the tables above. We compute Recall and Precision as a specific threshold t - considering any point with P(y|x) > t as being predicted to be the positive class (1) and <= t to be the negative class (0). Compute and report the recall and precision for thresholds t = 0, 0.2, 0.4, 0.6, 0.8, and 1.

          y P(y|x) y P(y|x)
0 0.1 0 0.55
0 0.1 1 0.7
0 0.25 1 0.8
1 0.25 0 0.85
0 0.3 1 0.9
0 0.33 1 0.9
1 0.4 1 0.95
0 0.52 1 1.0
Beyond just calculating accuracy, we discussed recall and precision as two other measure of a classifier's abilities. Remember that we defined recall and precision as in terms of true positives, false positives, true negatives, and false negatives:
Recall = #TruePositives / (#TruePositives + #FalseNegatives)
and
Precision = #TruePositives / (#TruePositives + #FalsePositives)
Q2 Computing Recall and Precision [3pts]. To get a feeling for recall and precision, consider the set of true labels (y) and model predictions P(y|x) shown in the tables above. We compute Recall and Precision as a specific threshold t - considering any point with P(y|x) > t as being predicted to be the positive class (1) and <= t to be the negative class (0). Compute and report the recall and precision for thresholds t = 0, 0.2, 0.4, 0.6, 0.8, and 1.

y P(y|x) y P(y|x)
0 0.1 0 0.55
0 0.1 1 0.7
0 0.25 1 0.8
1 0.25 0 0.85
0 0.3 1 0.9
0 0.33 1 0.9
1 0.4 1 0.95
0 0.52 1 1.0
Beyond just calculating accuracy, we discussed recall and precision as two other measure of a classifier's abilities. Remember that we defined recall and precision as in terms of true positives, false positives, true negatives, and false negatives:
Recall = #TruePositives / (#TruePositives + #FalseNegatives)
and
Precision = #TruePositives / (#TruePositives + #FalsePositives)
Q2 Computing Recall and Precision [3pts]. To get a feeling for recall and precision, consider the set of true labels (y) and model predictions P(y|x) shown in the tables above. We compute Recall and Precision as a specific threshold t - considering any point with P(y|x) > t as being predicted to be the positive class (1) and <= t to be the negative class (0). Compute and report the recall and precision for thresholds t = 0, 0.2, 0.4, 0.6, 0.8, and 1.

Added by Lauren R.

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