Data Science

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A classifier predicts a categorical target variable while a regressor predicts a continuous response variable. Can we fit a regressor as a classifier? Let’s find out.

Outliers are mostly seen as not needed. We will see an example where this is not true.

Would you buy oranges weighed on an uncalibrated weighing scale? Then why would you trust an uncalibrated classifier?

The ROC curve is an important metric to compare classifiers. Learn how to draw one step-by-step.

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Imagine you are buying a car and you want to know about its mileage. You don’t want to go for the user reviews or the company’s claim of mileage. The option you are left with is to predict the mileage all by yourself. So, if you are an interested data scientist, why not give it a try?

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Imagine you have four classifiers with similar accuracies. Are they really similar? Plotting a learning curve might reveal a hidden side to these classifiers.

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You’ve created a classification model and come across a new concept called confusion matrix. However tough it may seem, a classification model evaluation is not complete unless you add in your confusion matrix.

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Imagine training a classifier on a dataset only to find your friend is almost as good guessing at the target label, that too without looking at the data. Is your classifier any good then?

Listen – this blog post explained Often, classification datasets have a mix of continuous and categorical data. The continuous data typically have problems such as outliers, noise and lack of a defined distribution.

At times it is necessary to create dataframes within a program. Learn three ways to create them.