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How to calculate predictive weights?

User: "bhawnath131"
bhawnath131
New Altair Community Member
Updated by Jocelyn
Hello!
I am using multiple variables (rainfall, temperature, wind speed, slope, distance to settlements, etc) for predicting fire events using different machine-learning algorithms. In the below-given example, I have used Random Forest to do so. Using 'Explain Predictions" this result was generated. In Support Prediction, we have Slope = 6.054 (0.378); Agriculture = 0 (0.074) where 6.054 and 0 are respective values and 0.378 & 0.074 are predictive weights.
Is there any method by which we can calculate the cumulative values of these predictive weights that can be converted to an equation? 
I have to input these weights into another software (ArcGIS) to analyze raster data. Is there any other way to use this model on raster images?

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    User: "MartinLiebig"
    MartinLiebig
    Altair Employee
    Accepted Answer
    Hi,
    short answer: you cannot.

    Longer Answer: If we talk about explainable ai we often talk about local influence factors. They basically answer the question "if this value would not be what it is, how big would be the impact on the prediction?" and then the higher the value, the more impactful the column is.

    It is important to know, that these are _local_ ones. Those influence factors differ, as you see, for one example compared to the other. This is a feature of the method. You can check the wei port of Random Forest to get global influence factors. But then again only because you have a general pattern it does not mean that this is also present locally.

    If you want to learn more about these methods i can recommend this ebook: https://christophm.github.io/interpretable-ml-book/ . It's the source for what I wrote in the Interpretations extension.

    Let me turn the question around a bit: Why do you need an equation? You have the random forest model, which is a function.

    Best,
    Martin

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