Limitations of machine learning

Machine learning models are very powerful. You can use them in many cases where rule-based programs fall short. Machine learning is a good first alternative whenever you find a problem that can't be solved with a regular rule-based program. Machine learning models do, however, come with their limitations.

The mathematical transformation in machine learning models is very basic. For example: when you want to classify whether a credit transaction should be marked as fraud, you can use a linear model. A logistic regression model is a great model for this kind of use case; it creates a decision boundary function that separates fraud cases from non-fraud cases. Most of the fraud cases will be above the line and correctly marked as such. But no machine learning model is perfect and some of the cases will not be correctly marked as fraud by the model as you can see in the following image.

If your data happens to be perfectly linearly-separable all cases would be correctly classified by the model. But when have to deal with more complex types of data, the basic machine learning models fall short. And there are more reasons why machine learning is limited in what it can do:

• Many algorithms assume that there's no interaction between features in the input
• Machine learning are, in many cases, based on linear algorithms, that don't handle non-linearity very well
• Often, you are dealing with a lot of features, classic machine learning algorithms have a harder time to deal with high dimensionality in the input data