A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain haemorrhage types.
You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person.
This is an example of which type of machine learning?
A. clustering
B. regression
C. classification
It’s not classification but clustering. This was on MS official prep test on Coursera.
The classification is the correct answer. For this use case, The brain scan images and the brain haemorrhage types (labels) are provided to the model as the training set and so the model can predict the type on an unseen scan image, This is a supervised learning problem that requires a multi-class classification model
Clustering is a form of machine learning that is used to group similar items into clusters based on their features. For example, a researcher might take measurements of penguins, and group them based on similarities in their proportions.
https://docs.microsoft.com/en-us/learn/modules/create-clustering-model-azure-machine-learning-designer/introduction
Classification is a supervised machine learning technique used to predict categories or classes.
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/introduction
Clustering is an unsupervised machine learning technique used to group similar entities based on their features.
https://docs.microsoft.com/en-us/learn/modules/create-clustering-model-azure-machine-learning-designer/introduction
Regression is a supervised machine learning technique used to predict numeric values.
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/introduction
The explanation look clustering.
Clustering is a form of machine learning that is used to group similar items into clusters based on their features. For example, a researcher might take measurements of penguins, and group them based on similarities in their proportions.
Thot clustering is the answer. Any comments?