In machine learning, explain the importance of splitting up the dataset. What are the different w...
In machine learning, explain the importance of splitting up the dataset. What are the different ways to split and how should an analyst split the data.
Homework Answers
The importance of splitting up the dataset is that you need samples the machine has not seen before to asess its performance.
Because the machine will perform good on samples it was trained upon.
If you train your model on the entire dataset , then there is a chance of overfitting.
Analysts usually split the data into training set and testing set in a ratio of 70-30.
There is a function called train_test_split in sklearn.nodel.selection if you are using python to do machine learning.
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