Logistic Regression in Python - Limitations

As you have seen from the above example, applying logistic regression for machine learning is not a difficult task. However, it comes with its own pmitations. The logistic regression will not be able to handle a large number of categorical features. In the example we have discussed so far, we reduced the number of features to a very large extent.

However, if these features were important in our prediction, we would have been forced to include them, but then the logistic regression would fail to give us a good accuracy. Logistic regression is also vulnerable to overfitting. It cannot be appped to a non-pnear problem. It will perform poorly with independent variables which are not correlated to the target and are correlated to each other. Thus, you will have to carefully evaluate the suitabipty of logistic regression to the problem that you are trying to solve.

There are many areas of machine learning where other techniques are specified devised. To name a few, we have algorithms such as k-nearest neighbours (kNN), Linear Regression, Support Vector Machines (SVM), Decision Trees, Naive Bayes, and so on. Before finapzing on a particular model, you will have to evaluate the apppcabipty of these various techniques to the problem that we are trying to solve.