From the course: CompTIA SecAI+ (CY0-001) Cert Prep
Unlock this course with a free trial
Join today to access over 25,600 courses taught by industry experts.
Model validation
From the course: CompTIA SecAI+ (CY0-001) Cert Prep
Model validation
Once a model has been trained, validation determines whether it is truly ready for real-world use. Model validation is a process of testing a model on data it has not seen before. It ensures the model learns general patterns instead of memorizing training examples. This step is crucial in cybersecurity, where real-world data changes constantly. Before models are trained, the data that is fed to the model is usually split into training, test, and validation sets. The training set is what the model uses to learn patterns and relationships within the data. The validation set is used to adjust model parameters and monitor performance during training. The test set is used at the end to evaluate how well performs on data it has never seen before. If a model performs very well on the training data but poorly on the validation data, it has likely overfit the training data. This is an important concern in model validation. A model that has overfit is a model that is too closely tailored to the…
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.
Contents
-
-
(Locked)
The AI lifecycle1m 39s
-
(Locked)
Business alignment in the AI lifecycle1m 43s
-
(Locked)
Data collection2m 20s
-
(Locked)
Data preparation3m 15s
-
(Locked)
Model development and selection2m 13s
-
(Locked)
Model evaluation and validation2m 29s
-
(Locked)
Model deployment and integration3m 25s
-
(Locked)
Monitoring and maintenance3m 19s
-
(Locked)
-
-
(Locked)
Manipulating application integrations4m 8s
-
(Locked)
AI supply chain attacks2m 4s
-
(Locked)
Insecure plug-in design2m 9s
-
(Locked)
Insecure output handling1m 23s
-
(Locked)
Output integrity attacks2m 8s
-
(Locked)
Model denial of service1m 31s
-
(Locked)
Excessive agency1m 33s
-
(Locked)
Overreliance1m 34s
-
(Locked)
AI hallucinations1m 4s
-
(Locked)
-
-
(Locked)
Monitoring prompts and responses2m 51s
-
(Locked)
Log monitoring4m 30s
-
(Locked)
Rate and cost monitoring5m 1s
-
(Locked)
Auditing for AI hallucinations3m 33s
-
(Locked)
Auditing for accuracy3m 29s
-
(Locked)
Auditing for bias and fairness4m 35s
-
(Locked)
Auditing access and security compliance3m 48s
-
(Locked)
-
-
(Locked)
Responsible AI5m 29s
-
(Locked)
AI risks2m 23s
-
(Locked)
Introduction of bias2m 37s
-
(Locked)
Accidental data leakage2m 53s
-
(Locked)
Reputational loss2m 11s
-
(Locked)
Accuracy and performance of the model2m 22s
-
(Locked)
Intellectual property risks3m 31s
-
(Locked)
Autonomous systems2m 27s
-
(Locked)
Shadow IT and shadow AI1m 48s
-
(Locked)
Awareness training2m 21s
-
(Locked)