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Since your outcome $y$ is restricted, you should check „beta regression“ in which the outcome is „squeezed“ into the interval $ \hat{y} \in [0, 1]$. See an example in R here: http://r-statistics.co/Beta-Regression-With-R.html To my best knowledge, there is no "off the shelf" NN equivalent of beta regression. You could of course use a "normal" NN for ...


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You are dealing with noisy labels. I would not switch the labelling according to a trained model that learned on that particular data set, since probably you don't know which patterns lead to your models decision. Otherwise if you know the reason for the wrong labelling, you could try to build methods yourself that run a sanity check on your data. ...


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Loss function For most optimization algorithms, it is desirable to have a loss function that is globally continuous and differentiable. Two very commonly used loss functions are the squared loss and absolute loss. However, the absolute loss has the disadvantage that it is not differentiable at 0. The squared loss has the disadvantage that it has the ...


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The concept of a loss function comes from decision theory. Although there are some 'classic' loss functions the point is to be subjective, in the sense of being flexible enough to represent any particular problem conctext. So in that sense, yes, loss functions can be customised. One of the main ways this has been achieved is via Bayesian regression, as the ...


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First, you need to convert the dataframe in numpy array or tf.data dataset that the model understands. For this purpose, the tutorial provides you with a function: # A utility method to create a tf.data dataset from a Pandas Dataframe def df_to_dataset(dataframe, shuffle=True, batch_size=32): dataframe = dataframe.copy() labels = dataframe.pop('target') ...


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In RNN sequence is utmost important. So strict no for shuffling as it will break the sequence. Though I think, it will be good if we can shuffle different batch series, such that if a particular series used in batch i, next time that series can be the part of some different batch say j. If you try, please share your results.


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LSTMs have memory, so it matters in what order the model sees your samples. From the answer you linked: The model's internal parameters are changing and persisting with each new example it sees. The current prediction depends on the last prediction. Recurrent neural networks have memory, so order matters. If you're worried that your estimate of loss is ...


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Shuffling data would not seem to make sense here, since your model has "memory". You're not predicting $y_i$ from only $x_i$, but also $x_{i-1}$ and $x_{i-2}$. If you shuffle the data and perform prediction, you are implying that $x_1, x_2, x_3$ should give the same value as $x_2, x_1, x_3$ or $x_9, x_5, x_3$, or any series of values that merely ends in $x_3$...


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If I understand it correctly, you are asking how log likelihood in a multi-class classification problem relates to the cross entropy loss. So here is my try: Assuming we have a multi class classification problem ($C$ different classes) where we estimated the conditional probabilities for each class given the data $x$ (e.g., using a neural network) and where ...


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The error is self-explanatory. You provide the model with only 3 features whereas it needs 12 features. In model.py you select 3 features from the dataset, indeed. However, you apply one-hot encoding that creates new columns. Each new column describes only one category and contains values 0 and 1: whether this category is observed in a sample or not. And the ...


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I believe it will be difficult to answer this question w/o knowing the underlying data. Let's suppose, N1 is from men's football and N2 is from women's football history then both should be treated as separate data entity or should be mixed to create train/test set if we have a compelling need. What I will suggest - Check the Mean, Max, Min, ...


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What you are looking for are time series models that are called "dynamic regression models." These can include forecast distance models like linear models or tree-based methods. While it is possible to do classification with time series models, it is rarer still to do multiclass classification for time series. If you have a high number of classes to predict,...


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This is a classification problem, not a regression problem, so your first instinct was correct. For most models, you can easily get the probability estimate for each prediction. If you did tree based methods or a linear model, you would have probabilities easily. However, because of how an SVM works, it does not automatically output a probability estimate. ...


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It sometimes happens that a classification model will output probability estimates that are all in the low range. That means the model does not make any predictions that it is very sure are the positive class. Since only 10% of your data falls into the positive class, it appears to be a difficult problem to predict using your model. Do not transform the ...


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There is an ongoing competition on Kaggle right now which is about sales prediction. https://www.kaggle.com/c/m5-forecasting-accuracy/notebooks The user kyakovlev have created a series of notebooks where he creates features and creates an lgbm model: https://www.kaggle.com/kyakovlev/m5-three-shades-of-dark-darker-magic The notebook linked is the last in ...


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I understood it wrong ,here is the paper which discuss using multiple data set for the same classifier- http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.141.3142&rep=rep1&type=pdf They conclude- " We theoretically and empirically analyzed three families of statistical tests that can be used for comparing two or more classifiers over ...


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When should you re-train? Theoretically, a model will only degrade (become outdated and no longer useful) if the system you are modelling or the nature of the data has changed. Ideally you can spot this by setting up automated monitoring of the model in production. This could mean that predictions on new incoming data will be compared with the ground-truth ...


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Usually it worked for me that if the search space was know then annealing rate (divide the size size with number of iteration)helped to decrease/increase the step size gradually to get to local max/min but the draw back is it might get stuck in local and might need some "momentum" to go on, another draw back it it might be very slow.however it doesn't seems ...


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