Discuss how lowering cutoff value relates to changes

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Reference no: EM133370245

The data file OwnerExample.csv contains a small set of predictive model validation results for a classification model on rider mowers, with both actual values and propensities. In R, load the data and calculate predictive model accuracy, error rates, sensitivity and specificity using cutoffs of 0.0, 0.25, 0.5, 0.75, and 1.0 respectively. You can add more cutoff values in between the specified intervals to see changes. Discuss how lowering cutoff value relates to changes of accuracy, sensitivity, and specificity.

Actual	Propensity
owner	0.9959
owner	0.9875
owner	0.9844
owner	0.9804
owner	0.9481
owner	0.8892
owner	0.8476
nonowner	0.7628
owner	0.7069
owner	0.6807
owner	0.6563
nonowner	0.6224
owner	0.5055
nonowner	0.4713
nonowner	0.3371
owner	0.2179
nonowner	0.1992
nonowner	0.1494
nonowner	0.0479
nonowner	0.0383
nonowner	0.0248
nonowner	0.0218
nonowner	0.0161
nonowner	0.0031

Reference no: EM133370245

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