ITM 618 Chapter Notes - Chapter 1: Data Science, False Positives And False Negatives, Churn Rate

• School: Ryerson University
• Department: Information Technology Management
• Course: ITM 618
• Professor: Mehdi Kargar

Document Summary

Extracting knowledge from data to solve business problems. Data science & data mining: data science is a set of fundamental principles that guide the extraction of knowledge from data, data mining is the extraction of knowledge from data, via technologies that incorporate these principles. Prediction model: classification model, class-probability estimation model, regression model. Class-probability estimation model: frequency-based estimate of class membership probability, laplace correction: to moderate the influence of leaves with only a few instances. p(c) = (n+1)/(n+m+2) Example: promotion cost: , product-related cost: , product price: . Correct & error rate: true positive rate, true negative rate, false positive rate, false negative rate. When the instance is actually positive: true positive rate, false negative rate. When the instance is actually negative: true negative rate, false positive rate. Sensitivity & specificity: sensitivity: (true positive rate) Sensitivity = fn , false negative , . Specificity = fp , false positive , .