I often come across notebooks showing a regression prediction where they start off by normalizing features to make them normally distributed , I find conflicting answers online , so my question is when is it necessary to have normally distributed features?
Hello Bensalem,
The answer depends on the model-building method chosen.
For regression methods, the concern is that model residuals are normally distributed, not that each IV/feature score have a normal distribution.
Good luck with your work.
The requirement for features in regression prediction to follow a normal distribution hinges on the particular algorithm employed and the inferences it establishes. The following are some crucial aspects to remember:
1. Linear regression prerequisites: Linear regression presumes a linear correlation between independent and dependent variables, and it also assumes that the residuals (the deviation between estimated and true values) follow a normal distribution. As a result, for this model, it's crucial that features follow a normal distribution.
2. Non-linear regression: The assumption of normality might not be as crucial when using a non-linear regression algorithm like polynomial regression. Nonetheless, assessing the features' distribution and deciding if they need transformation or scaling to enhance the model's efficiency is still crucial.
3. Different algorithms: Some machine learning algorithms like decision trees or random forests don't insist on the normality of features. Such algorithms can accommodate non-normal features, and they are more resistant to data distribution.
4. Scaling of features: Even if normality is not mandatory, scaling or normalizing features to a comparable range often proves advantageous. This can facilitate faster algorithm convergence, enhance understandability, and avert large-scale features from overpowering the model.
To summarize, the requirement for normally distributed features hinges on the particular algorithm and its inferences. While normality is crucial for linear regression, other algorithms might offer more flexibility. Nonetheless, scaling or normalizing features can still prove advantageous in enhancing model efficiency. It's advisable to keep in mind the unique requirements and presumptions of the algorithm employed and make decisions based on those.
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