Last week, I had an hour-long chat with my friend Ken Jee for his podcast. Starting tomorrow, you can watch the conversation on the Ken’s Nearest Neighbors YouTube channel!
👉 Tip #14: How to ensemble your ML models
When working on a Machine Learning problem, it’s always a good idea to try different types of models to see which one performs best.
However, you can also use a process called “ensembling” to combine multiple models. The goal is to produce a combined model, known as an ensemble, that performs better than any of the individual models.
The process for ensembling is simple:
- For a regression problem, you calculate the average of the predictions made by the individual regressors and use that as your prediction.
- For a classification problem, you can either average the predicted probabilities output by the classifiers, or you can let the classifiers vote on which class to predict.
The idea behind ensembling is that if you have a collection of individually imperfect models, the “one-off” errors made by each model are probably not going to be made by the rest of the models. Thus, the errors will be discarded (or at least reduced) when ensembling the models.
Ensembling in scikit-learn
Here’s a simple example in which I ensembled Logistic Regression and Random Forests using scikit-learn’s VotingClassifier:
Notice that the accuracy of the ensemble (0.725) is significantly better than the accuracy of either individual model. (Check out the full code here.)
Advice for ensembling
➡️ Ensembling is useful any time model accuracy (or another evaluation metric) is your highest priority. Keep in mind that the ensemble will be less interpretable than the individual models.
➡️ It’s ideal to include at least 3 models in the ensemble.
➡️ It’s important that all models you include are performing reasonably well on their own.
➡️ It’s best if the included models generate their predictions using different processes, since they will be likely to make different types of errors. (This is what makes Logistic Regression and Random Forests good candidates for ensembling!)
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See you next Tuesday!
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