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It should work: the variable is ordinal so using numerical values makes sense. So there's a bug somewhere, here are a few suggestions of things to look at: Possibly a type conversion error somewhere: make sure the variable is interpreted as numerical. Check whether the model actually uses the variable: if not then it's likely some type error; if yes then I ...


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Yes, the typical approach is to obtain the saliency map of the input, which are "heatmaps" of the contribution of each pixel to the final classification. In this free online book about Explainable ML, you can find the most relevant approaches to obtain saliency maps, like vanilla gradients, together with other pixel attribution techniques. Here you ...


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In very simple words, Those models which use all the features of the instances will suffer from irrelevant features e.g. Neural Nets, KNN etc. While the models which have an internal strategy to compares the features to decide the best while training will not suffer(at least for this reason) e.g. Tree Here is a snap from "The Elements of Statistical ...


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The first thing that comes to my mind is that you might have not normalized your features correctly. Generally a feature ranging between a bigger range of values, compared to the other ones, is going to be more influencial in terms of the models' output. In order to midigate this issue, one common practise is to transform your features into having zero-mean ...


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This has something to do with Mutli-colinearity in case if Multiple Linear Regression. Beacause, Keeping k dummies for k levels of a categorical variable is good idea, but there is a redundancy of one level, which is here in separate column. This is not needed since one of the combination will be uniquely representing this redundant column. Hence, its better ...


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This I how did to tie the feature importance values to column names hd = list(XData.columns) for i, f in zip(hd, best_result.best_estimator_.feature_importances_): print(i,round(f*100,2))


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Ages I guess adding up the ages won't makes sense, since the number 30 means that there is one person of 20 years and one of 10, or a bunch of 5 years old? What you could do is have number_of_adults_in_household and number_of_children_in_household. This way you still aggregate the ages but keep more information. Education Education might be a more difficult ...


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