Data Analytics modelling, why tune by hand?

When we’re carrying out analysis once we’ve got clean transformed data we have to create a model.

There are many types of models that can be used depending on the type of analysis or prediction being made.  For instance, predicting a class, predicting values,  finding unusual points.

Within each collection of models I’d really like to be able to spin through the models and selectively apply each to my dataset.   I want to see the Accuracy, p-Value, Sensitivity, Specificity etc.. ranked.

With the model algorithms already pre-baked why can’t we just consume them in a fairly efficient way?

Of course we can do this by hand with Python or R but it would be much better if the software handled this type of plumbing/set-up.

Here’s an example of doing it with R from Suraj V Vidyadaran.  He cycles through 17 classification algorithms applying them and outputting a confusion matrix for each one.  This is a great resource for learning R but it also shows how there are patterns in the modelling that be abstracted away, in my opinion.

 

 

About Lee Hawthorn

Data Professional
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