I know that neural networks including the deeper one perform better when we augment the dataset size,
I wonder which of the other machine learning algorithms used for regression is sensitive to the size of data, or it is not sensitive.
You can have a look at this article
https://www.google.com/url?sa=t&source=web&rct=j&url=https://www.mdpi.com/2076-3417/11/2/796/pdf&ved=2ahUKEwighqbAnLzxAhXHzjgGHR1uCooQFjAMegQIIxAC&usg=AOvVaw3qcT8FOh4aK1eunK4T9dny
Also you can read this
https://machinelearningmastery.com/much-training-data-required-machine-learning/
Generaly all of them are sensitive unless you are using a rule based approach.
You estimate parameters from a sample and you are going to predict for the whole population. Parameter estimation is always error-prone. More data means large sample.
Hi Nana Cne!
All machine learning algorithms are volume-sensitive data sets. But, among all the algorithms, SVM can work with a smaller data set.
I can help you with the development of neural networks models for your task.
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