Evaluating your decision tree performance - 7.3

Machine Learning

Version
7.3
Language
English (United States)
Product
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Real-Time Big Data Platform
Module
Talend Studio
Content
Data Governance > Third-party systems > Machine Learning components
Data Quality and Preparation > Third-party systems > Machine Learning components
Design and Development > Third-party systems > Machine Learning components
This section explains how to evaluate the results of your decision tree.

Below is a confusion matrix using the data from your test Job.

The model tries to predict (conversion = no) as being either true of false.

• TN = 15
• TP = 446
• FN = 12
• FP = 41
• Accuracy = (TP+TN)/Total = (15+446)/(446+15+12+41) = .90
• Sensitivity = TP/(TP+FN) = (446)/(446+12) = .97
• Specificity = TN/(TN+FP) = (15)/(15+41) = .27

When you tested the tree model:

• It was correct 90% of the time (accuracy)
• It accurately predicted 97% of those persons who did not result in a conversion (sensitivity)
• It accurately predicted 27% of those persons who did result in a conversion (specificity)