Why Don't We Call It Logistic Classification Instead?
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Why Don't We Call It Logistic Classification Instead?
Start questioning the names of other algorithms too. It will be a fun activity.
Have you ever wondered why logistic regression is called "regression" when we only use it for classification tasks? Why not call it "logistic classification" instead? Here's why.
Most of us interpret logistic regression as a classification algorithm. However, it is a regression algorithm by nature. This is because it predicts a continuous outcome, which is the probability of a class.
It is only when we apply those thresholds and change the interpretation of its output that the whole pipeline becomes a classifier.
Yet, intrinsically, it is never the algorithm performing the classification. The algorithm always adheres to regression. Instead, it is that extra step of applying probability thresholds that classifies a sample.
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Why Don't We Call It Logistic Classification Instead?
I don't like when you provide a list of loads of python libraries to try, I ignore these emails. What I love about your posts is that you provide a single easily digestible tip. Providing a big list of things you could try that would take ages to read through seems to defeat the point.
what I find lacking in all data science blogs that I read concerns the conditions when an analyst begins to take steps outside the objective rules of science and begins to place more faith in the model itself than its general utility at solving a problem. how should analysts deal with the tendency to wish to over fit a model to increase its accuracy? What objective rules can analysts follow to retain negative feedback data in their models rather than adjusting the model to accomodate for outliers?