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TP vs FP rate at one ROC Curve
decision threshold
TP vs FP rate at The Receiver Operating Characteristic (ROC) curve is a
another decision graphical representation that illustrates the performance of a
1 threshold
binary classifier system at varying threshold values. It plots the
True Positive Rate (TPR) against the False Positive Rate (FPR) at
various threshold settings.
TP Rate This curve plots two parameters:
True Positive Rate (Sensitivity) is the proportion of actual
•
positive cases that are correctly identified by the classifier.
TP
TPR =
TP + FN
0 • False Positive Rate is the proportion of actual negative
0 FP Rate 1 cases that are incorrectly classified as positive.
FPR = FP
FP + TN
To generate an ROC curve, you need to perform the following tasks:
• Vary the threshold of your classifier, usually ranging from 0 to 1, and calculate TPR and FPR at each threshold.
• Plot these TPR and FPR values on a graph. TPR is plotted on the y-axis, and FPR is plotted on the x-axis.
Lowering the classification threshold classifies more items as positive, thus increasing both false positives and true
positives. The following figure shows a typical ROC curve.
Evaluate: Exoplanet Use Case
At this particular stage, we may need to
evaluate the model to find out the accuracy of
the model that makes the best prediction.
ROC is a metric that is used to find out the
accuracy of the model.
Figure shows the accuracy of the model using
ROC curve.
What is Deployment?
The deployment phase is the last stage of the AI project cycle and is used when the AI model is put into use in
a real-world setting. This involves integrating the model into existing systems or applications, such as creating
Application Programming Interfaces (APIs) or embedding it directly into software. It also includes setting up the
necessary infrastructure, like servers or cloud services, to support the model. Once integrated, the model needs
to be able to process new data and provide predictions. Monitoring tools are established to track the model’s
performance and ensure it works correctly. Logging and reporting are also important to capture data on how
the model is performing and to identify any issues that might arise. This phase is crucial for making the AI model
functional and useful for end-users.
Deployment of an AI project is an essential phase in bringing the created AI solution into practical use. The
following are some of the primary reasons why AI project deployment is relevant:
• Deployment converts theoretical models into actual tools that are capable of being used in real-world
circumstances, bringing the AI solution into practice.
AI Reflection, Project Cycle and Ethics 135

