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A way to gauge, how useful a predictor $x_j$ is within a given model $M$ is by comparing the performance of the model $M$ with and without a predictor $x_j$ being included (say model $M^{-x_j}$). If we have multiple predictors though we are face with a situation we would have to create $p$ different $M^{-x_j}$ models going back and forth. The cost of this re-...


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Sample without replacement, i.e. we get a scrambled/"permuted" version of $x_j$.


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By a simulation experiment, I discovered that when the sample size is large, the performance metrics calculated in the two ways (OOB and validation set) are numerically very very similar (the maximum difference is 0.1). I have tried with accuracy, sensitivity, specificity and F1.


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First, let's make clear here that the base learners most often don't know about the loss function: they most often do not try to minimize it. The loss function is from the boosting part, the base learners are agnostic to it. The only way they can be exposed to the loss function is through the surrogate targets we train them on. The problem with Bernoulli I ...


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With tree regression, you can be a little more relaxed about assumptions. In particular, you simply give up on the "linearity" (or more precisely, the correct functional specification) assumption, because the natural process obviously does not follow the piecewise flat segmentation that is assumed by the tree model. Instead, you simply acknowledge ...


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Decision tree are extremely efficient, very easy to tune and can handle non-linearity extremely well. Adaboost and RandomForest should be better than Decision Tree but they might be overfitting a bit. If the exemple of feature is correct and you have that many precision digits, then all the more for Trees because they can split on specific values that would ...


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I am not aware of this method already existing, but assuming a continuous, or at least ordered response, this sounds like tree boosting with a learning rate of 1, maximum tree depth of 1 and a maximum of 5 iterations. (If you have a response from a different GLM family, the same applies, but obtaining the pseudo-response will involve slightly more ...


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Your problem is not with the random forest, but with the MAPE. > test_set[,6] [1] 48.496420 88.210020 26.921240 5.298329 2.362768 49.069210 1.312649 25.107400 > pred_rforest 4 5 8 11 16 20 21 24 29.96306 34.66228 28.90458 26.67681 19.65220 36.34482 22.29752 36.44267 Several of your predicted-...


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The answer to your questions are both Yes. For 1. Consider that you have a trained classifier, then you just need to do what is explained in this link tutorial. For what concerns the second question, if you have in mind values of this parameter and store them in a dictionary, where the key is named “ccp_alpha”, you will be able to grid search the values. ...


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