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Typically imputation will relate to filling in attributes (predictors, features) rather than responses, while prediction is generally only about the response (Y). Even if imputation is being used to refer to filling in Y's the purpose is different; you're not using it for the primary purpose of getting a prediction for that Y.


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From the topology of this example it looks like the KNN should be good at distinguishing green and turquoise, because knn is strong when local relative density of a class is the best predictor and when a given distance has the same meaning at any point in space, and in any direction. SVM here should be strong at making the difference between points ...


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If I understand your problem correctly, you want your model to predict a greater variability so that you can use this in simulations? If so, I was faced with a similar problem and the approach in this paper was useful to me: Model Calibration Under Uncertainty - Matching Distribution Information by Swiler, Eldred and Adams. Using this approach as a ...


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To address your second question (or possibly third, since there's one in the middle you've not counted): As far as I'm aware, there is no guarantee that stacking will always lead to better performance (i.e. lower prediction error). It tends to improve performance on average, for reasons well explained in this answer. Note that the same answer also implies ...


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If I understand you correctly, I think one distinction that has been made in the literature is of that between discriminative models which learn $p(y|x)$ and generative models which learn $p(x,y)$. The most thorough theoretical and experimental treatment of this distinction see this study


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Estimating linear regression models, via OLS, and fitting distributions can both be accomplished using the same method, Maximum Likelihood Estimation (MLE), and Yes, you are correct on this. When using maximum likelihood, we are always fitting some kind of distribution to the data. The difference is however between the particular kinds of ...


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Whether conditioning on specific $X$ regimes makes sense is conceptually independent of whether you only observe a small subset of your actual outcome. It may make sense (or not) whether you are predicting "far" or "soon" out of your training sample. So, by all means, if you believe it makes sense, then try it. However, don't expect magic from this model. ...


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Honestly your questions has only one good answer which is it depends :D. However i will try to give you another idea. You could use median of means, https://arxiv.org/pdf/1711.10306.pdf (don't know if it is the best article to speak about it, again it is just to give the idea). The main idea behind median of means is what you say, media is robust, means ...


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According to Schwartz 1, the Rayleigh distribution has been used to model the distribution of bullets hitting a target. The model assumes the bullet hits are gaussian distributed along the target’s horizontal axis. Likewise and independently, the bullet hits are gaussian distributed along the target’s vertical axis. The two distributions are centered on the ...


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By default, caret will estimate a tuning grid for each method. However, sometimes the defaults are not the most sensible given the nature of the data. The tuneGrid argument allows the user to specify a custom grid of tuning parameters as opposed to simply using what exists implicitly.


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It might worth adding another, perhaps more straightforward example to Stephen's excellent answer. Let's consider a medical test, the result of which is normally distributed, both in sick and in healthy people, with different parameters of course (but for simplicity, let's assume homoscedasticity, i.e., that the variance is the same): $$\begin{gather*}T \...


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I hope you have followed the good advice in the comments. Why do you use downsampling? It is most often used to solve a nonproblem. See Why downsample? and many of its answers. With only 400k rows memory shouldn't be a problem, if it is, get some better software. Ther problem may be the use of accuracy, which is an improper score function, see Is accuracy ...


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Effect size applies to the effect you are measuring. As the examples you put in your question demonstrate, even within the field of churn, there are different effects that are interesting and the way to measure them will vary. As to small, medium and large, Cohen just came up with some guidelines to use when you don't know what to do; I believe he did this ...


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If you are considering linear models, and are concerned with overfitting, you can consider using linear regression with regularization ie. ridge regression or lasso or a combination("elastic net"). If you want to try out non-linear as well as interaction terms, you can try SVM regression with a polynomial kernel or an RBF kernel. This will still require ...


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The problem with daily data and only 250 days is that you could face seasonality issues that you can't really evaluate statistically, only by business knowledge. But regardless of seasonality, 250 samples and 10 features are quiet enough in my opinion to build a predictive modeling. The best way to do it is use boosted regression (see xgboost, does a great ...


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Somers' $D_{xy}$ is a general measure of predictive discrimination which can handle binary, ordinal, and censored time-to-event outcome variables. In the binary $Y$ case it is just $2 (c - 0.5)$ where $c$ is the concordance probability, AKA area under the ROC curve. It is a very interpretable measure of predictive discrimination but like AUROC is not ...


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Check for integers - if the continuous data happen to be integers, then they may be confused with categorical. Normalising the continuous data will reduce the risk of this error. Consider adding binning and bagging, dealing with outliers, transform skewed datasets, PCA/factor analysis etc Deal with time-series data After doing the above, reducing the ...


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