Splitting Training and Test Data for Machine Learning Using Python and Scikit Learn tutorial
Welcome to the video series on Introduction to Machine Learning with Scikit Learn and Python. This is Chapter -7 and in this chapter, we will talk about how to judge the performance of our machine learning algorithm.
This is a video series on scikit learn tutorial. In this series I’m talking about using scikit learn machine learning for our implementations
Machine learning Algorithm selection faces a unique catch22 situation where you get the data to train but need unseen(new)data to test the algorithm which is available only with production.
To avoid this situation and understand the performance of the selected Machine Learning algorithm, we need to generate TEST DATASET from the available DATA Set.
We can do the same by segregating the available dataset in Training Data Set and Testing Data Set. Scikit Learn provides a utility function called train_test_split which can help us to achieve this goal
This video explains the usage of train_test_split function and how we can generate training and testing datasets.
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