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In ordinary linear regression, we are estimating the mean of some variable y, conditional on the values of independent variables X. As we proceed to fit the ordinary least square regression model on the data we make a key assumption about the random error term in the linear model. Our assumption is that the error term has a constant variance across the values of independent variable X.
For any machine learning problem, say a classifier in this case, it’s always handy to create quickly a base line classifier against which we can compare our new models. You don’t want to spend a lot of time creating these base line classifiers; you would rather spend that time in building and validating new features for your final model. In this post we will see how we can rapidly create base line classifier using scikit learn package for any dataset.…Continue