- Course overview
- Search within this course
- What is machine learning?
- ML in drug discovery: why now?
- ML in the drug discovery pipeline
- Getting started in ML using WEKA
- Hands-on with WEKA
- Other tools utilising ML or NLP for drug discovery
- Summary
- Your feedback
- Learn more
- References
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Model performance
Since this is a multi-class classification problem, the most common approach is calculating the performance in predicting each class, then calculating the average of performance weighted by the number of samples in each class.
The Classifier Output window shows a summary of the model’s performance using different performance metrics. It shows that the models correctly classified 97.5% of the instances. It also shows the models performance in predicting each type of cancer. The model was able to predict PRAD cancer with 100% accuracy. The lowest performance of the model is 95.5% in predicting COAD cancer. The overall weight average performance is 98.4% in predicting all types of cancer. (explain different parts of the output).
