End-to-End Machine Learning: kappa metric in R

When training a machine learning model, it’s important to evaluate its performance to understand how well it will work on new, unseen data. One common way to evaluate the performance of a model is by using a metric called “kappa.”

Kappa is a measure of the agreement between the predictions made by a model and the true outcomes, taking into account the chance agreement. The kappa statistic ranges between -1 and 1, where 1 means perfect agreement and -1 means perfect disagreement. Kappa is often used in fields such as medicine, psychology, and sociology to evaluate the performance of diagnostic tests.

In R, there are several ways to calculate kappa, and several libraries such as caret, mlr, etc. which provide functions to calculate kappa. Some of the most popular functions are kappa2(), cohen.kappa() and quadkappa() that can be used to calculate kappa.

It’s important to note that Kappa is a more robust metric than accuracy when the classes are unbalanced, as it takes into account the chance agreement. Kappa is also a good metric when the model has to classify into more than two classes.

Overall, Kappa is a more robust metric than accuracy for evaluating the performance of a machine learning model, particularly when the classes are unbalanced or when the model has to classify into more than two classes. It’s a good way to measure the agreement between the predictions made by a model and the true outcomes, taking into account the chance agreement.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: End-to-End Machine Learning: kappa metric in R.

End-to-End Machine Learning: kappa metric in R

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