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Lets break down your questions into sub-parts. Q1: do you mean the predictor needs to predict 2 target values? (Target 1 and target2) Answer: the model can always predict only ONE target value. This target value can be either categorial or numerical. Q2. One column ( train_dataframe) contains all data from previous dates. Why? Ans: we don’t need to do ...


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First question; is it possible to pass an entire data frame as a variable to a model? No, each feature must be a single value. In other words you could provide the data frame as a vector containing all the values, assuming the size is fixed: each column would correspond to a specific cell in the original data frame. But I think an even better option in ...


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Not currently, though hopefully very soon. https://issues.apache.org/jira/browse/ARROW-3750 is in progress and hopefully will resolve in the coming weeks.


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If the dimensions are not linearly correlated, you may use an autoencoder to perform the dimensionality reduction. Just like PCA that can perform a reconstruction, but with non-linearity. Then, you can perform classification with the latent space. Autoencoder is a multi-dimensional auto-regressive model with a dimensional bottleneck somewhere in the middle....


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Something like this for instance: library(plyr) ddply(data,'region',function(x) {mean(x$age)})


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Which function are you using at Loss? Using the right one is important when dealing with imbalanced datasets. 7% is imbalanced, but not that bad. Have you tried any eXplainable Artificial Intelligence (XAI) method? Normally I use Shap. It is really good to see which feature contributes in which direction. You can see an example here. You can check the ...


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If you are looking to predict multiple time series (which would be similar in nature, since each weather station in the area would record similar temperatures, even if they are not identical), using a separate LSTM model for each may prove quite time-consuming. One approach you could take is one suggested in an excellent answer for another question on Cross ...


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Oversampling on your test set will only artifically improve your performance. What you may want to do instead is changing your objective function to give more importance to you imbalanced class. There are already a lot of question about class imbalance on this website, such as : Classification problem: custom minimization measure


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I think what you are dealing with is a type I vs. type II error discussion. It seems that you want to avoid Type II error. One way to go is to consider $F_\beta$ for your performance metric. It is a modified version of $F_1$. As seen here : https://en.wikipedia.org/wiki/F1_score, $F_\beta$ can be formulated in terms of type I /type II error. You then need ...


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One solution to make the recall more important whitout getting to the problem mentionned by @Jurgy is to use $F_\beta$, a modified version of $F_1$ where recall is considered $\beta$ time more important. As seen here : https://en.wikipedia.org/wiki/F1_score, $F_\beta$ can be formulated both in terms of recall/precision, and in term of type I /type II error. ...


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For a given class, you may usually treat the problem as a binary classification (the given class versus all others). You can do the same for your feature importance calculations. However we won't be able to help you much outside of a given problem and a given model, as available feature importance solution depends on the model used.


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I'm not sure it is the best way to do it in r, but you can create a vector simulated temperature for each days of the year by using your reference vector with few temperature by doing the following: 1) You set a dataframe containing few temperatures as references for each month (here, I used lubridate package to manipulate dates): library(lubridate) Date = ...


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What about creating a Pandas DataFrame and adding a new column such as "Temp_simulated" and simulate the temperature?


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