Avoid common mistakes when building ensemble models in Python

Ensemble models combine the predictions of multiple base models to improve overall performance. While they often lead to better results, building ensemble models can be tricky. In this thread, we’ll explore common mistakes people make when creating ensemble models with the correct code snippets to resolve these issues.

1. Not scaling the data:

A common mistake people make is to forget to scale the data before training the model, and failing to scale or normalize the data before using it for ensemble models can lead to biased results. Furthermore, different algorithms have different sensitivity to input data scales. Below is an example code that scales the data first and then applies the ensemble model.

2. Using similar models:

Ensemble models enhance predictive accuracy by leveraging the strengths of different algorithms. When building an ensemble model, one should incorporate diverse models with varying behaviors and underlying assumptions. Individuals make the mistake of relying on similar algorithms that can limit the ensemble’s ability to capture a wide range of patterns in the data. In the example code below, we have used two different models to enhance the accuracy.

3. Not tuning the hyperparameters:

Another common mistake when working with ensemble models is not properly tuning hyperparameters for individual base models. Hyperparameter tuning is crucial to ensure that each base model performs optimally and contributes effectively to the ensemble. Here is an example code that tunes two models before training them together: