The ultimate goal for any machine learning model is to learn from examples in such a manner that the model is capable of generalizing the learning to new instances which it has not yet seen.
So, when we approach a problem with a dataset in hand, it is very important that we find the right machine learning algorithm to create our model.
Model validation is the process that is carried out after Model Training where the trained model is evaluated with a testing data set.
I explained the model validation process and several popular validation methods.
Then I demonstrated how to validate machine learning model with different methods in Python.
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The Python code is uploaded into https://github.com/AIMLModeling/Machi...
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