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2

You don’t. It uses hashing trick, so the data is passed through a hashing function that maps the data to codes. The function can and will map different values to same codes, because in general it is used to reduce cardinality of your data. To learn what values were mapped to what codes, you need to create a dictionary by looping over your data, transforming ...


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Let $time_{i}$ as $y_{i}$ and the $\{\mathbf{x}_i\}_{i=1}^{N}$ as your predictors (stops) where $\mathbf{x}_i$ is equal to the row $i$ of your dataset taking the last columns out and $i$ is the row index. Take a deterministic regression problem where you want to fit: $y=f(x;\mathbf{\Theta})$, $\mathbf{\Theta}$ as an arbitrary dimensional parameter. Take $\...


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When you select variables using your "crude" method, this amounts to placing a prior on your coefficients that says that one specific subset of coefficients is (a priori) certainly equal to 0, and the others are (a priori) entirely unknown (i.e. a uniform prior). So the question is: is this a better prior than the Laplace prior that is implied by a ...


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Perfect multicollinearity among predictors should be dealt with in any regression, Cox or otherwise. Some software might just refuse to fit such data. Some functions are smart enough to find and remove enough predictors to ensure linear independence (perhaps silently), but you don't want to count on that and you presumably would prefer to make such a choice ...


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First, it is good to know that Multicollinearity problems are adressed by algorithms, but every algorithm has its own handling of multicollinearity. So you can be assured, that different algorithms deal with it in a different way, although they might be from the same family, like XGBoost, Catboost, LightGBM GradientBoosting from sklearn or the EBM by ...


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It is possible to compute a bound on the leave-one-out error of the SVM (I've used the Radius-Margin bound and the Span bound and found they work quite well). This is often better than using a validation set as it leaves more data for training, and is computationally efficient. If you are performing feature selection with the SVM, it may make generalisation ...


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The goal of propensity score matching (PSM) is to adjust for confounding by achieving covariate balance on a sufficient set of covariates required to nonparametrically identify the causal effect. Covariate balance is the degree to which the treatment is independent of the covariates, or, equivalently, how similar the covariate distributions are between the ...


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I imagine that with 1 million data points, the prior information is more or less negligible, especially if the model has no complex structure (e.g. hierarchical structure). The likelihood should -- assuming the model is simple -- overwhelm the prior. Why remove features at all? Do you have some other constraint to satisfy? Given the number of ...


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Many analysts automatically assume that feature selection is a good idea. This never followed. Parsimony is the enemy of predictive discrimination. Perhaps more important, feature selection, whether using lasso or other methods, is unreliable. The way to tell if lasso is good enough is to test its resilience/stability using the bootstrap. The bootstrap ...


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Lasso is a common regression technique for variable selection and regularization. By defining many cross validation folds and playing with different values of $\alpha$, you can find the best set of beta coefficients which confidently predicts your outcome without overfitting or underfitting. If the Lasso technique has assigned the beta coefficient of any ...


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Feature selection often makes generalisation performance worse rather than better, so I would recommend against routinely including it in the analysis unless identifying the relevant attributes is a specific aim. Regularisation (which is used in most modern ML algorithms) is able to deal with colinearity. Testing individual features for independence is not a ...


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