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I am working with vehicle occupancy prediction and I am very much new to this, I have used random forest regression to predict the occupancy values.

Random forest jupyter notebook

have around 48 M rows and I have used all the data to predict the occupancy, As the population and occupancy were normalized due to the higher numbers and I have predicted. I am sure the model is not good, how can I interpret the results from the RMSE and MAE. Also, the plot shows that it is not predicted well, Am I doing it in a correct way to predict the occupancy of the vehicles.

Kindly help me with the following,

  1. Is Random forest regression is a good method to approach this problem?
  2. How can I improve the model results?
  3. How to interpret the results from the outcome
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1 Answer 1

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Is Random forest regression is a good method to approach this problem?

Overall, decision trees tend not to be good regressors. But it can be that for your case it is working well. You need to evaluate the results corresponding to a metric and then compare different models.

I like MAE in regression models because it's very intuitive.

How can I improve the model results?

Note that decision trees don't need values to be scaled to perform well. Consider

Make sure you have meaningful features in the model.

Try different models with different hypeparameters.

If you have categorical features, use https://contrib.scikit-learn.org/category_encoders/

How to interpret the results from the outcome

If you have not scaled the target. And you are measuring MAE.

Let's say your MAE = 2. Then the mean error in your prediction is 2 passengers.

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