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splitting a data set into trained test and validation data sets is common practice but what are these data sets why do we split them and what is their use well a machine learningamp;#39;s performance is highly dependent on the data set it is being trained on that means even if it is on the same topic you can get different models by training the model on different data sets one thing data scientists strive for is to make models that are robust meaning they will give consistent results that are correct most of the time to do this we do the training testing and validation process letamp;#39;s see this process in a simplified way an accepted approach is to divide the data into 70 for training 20 for testing and ten percent for validation the model is trained with the training data meaning the parameters of the model are updated using these examples basically the model is optimized based on the version of the world represented in the training data once training is done we use the validati
