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Every one knows data-driven modeling, but what is model-driven (or non data-driven) modeling?

Data-driven methods are ones that rely on empirical observation, and produce models that map between observed inputs and observed outputs. Non-data-driven models can be built from domain knowledge or ...
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Linear regression assumptions

To some extent, I disagree with every one of these. Nonlinear regression models (e.g., SVM) do not assume a linear functional form, and nonlinear basis functions (e.g., polynomials or splines) can ...
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How does Catboost regressor deal with categorical features at predict time?

In a simplified way of putting it, we substitute the category id with the mean value of the training set target for this category. CatBoost implements some tricks like only using the preceding values ...
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What can be done with same samples with different target?

As others have explained for regression problems, leave them in. For categorical, I think the situation is a bit more nuanced. It could be that you are actually in a multi-label setting, or it could ...
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What can be done with same samples with different target?

in addition to the answer from @Dave, consider that usually we start an assumption on the conditional distribution $P(y|x)$, for example for regression we assume Gaussian noise... this means that the ...
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What can be done with same samples with different target?

This is completely normal; leave them in. An easy example is in an ANOVA problem (which can be viewed as a regression) where multiple subjects in the same group (so same group "value" where ...
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MAE divided by median metric

You are encoding a Laplace prior over your targets... now, by itself a loss has no much meaning, however, if you associate it with a distribution, you can understand how good it is the Mean Absolute ...
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Transformer-based architectures for regression tasks

Transformers are increasingly being used in the area of temporal forecasting, which is autoregressive by nature and thus a good fit. Lim et al. (2021) recently announced "temporal fusion ...
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How should a dataset looks like for Time series forecasting

Timeseries datasets should first be representative enough of the business process they are dealing with. For instance, some processes are better represented day by day, others, hours by hours, etc. ...
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How should a dataset looks like for Time series forecasting

What you described is a longitudinal panel dataset rather than timeseries data. As such you use methods for panel data regression, mostly fixed- or random effects. Good resources here and here.
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1 vote
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Loss function to prevent estimator bias

Thank you @Nikos M. for your suggestions. I was about to use your post-applied factor but then gave it another try. And found what caused this. It was that the final layer was using a ...
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1 vote

How is uncertainty evaluated for results obtained via machine learning techniques?

I am also not aware of any papers that 'prove' that ML, in general, works. Many techniques are based on optimization, distance measures, that really do not have any probability counterparts. I am also ...
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3 votes

How to use LAT/LNG as predictor variables

If you want to predict at locations where you don't have data, and you assume that there is a continuous surface of your variable of interest (ie it is defined at all locations) then you can use ...
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How is uncertainty evaluated for results obtained via machine learning techniques?

Here are some of the main approaches I'm aware of. One method is to use Bayesian machine learning, which learns a probability distribution over the entire parameter space (see Joris Baan's A ...
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Combine multiple duplicate categorical variables into a single one for multiple linear regression

The issue is just that you consider the list of actors as ordered, but if they are considered as an (unordered) set it works perfectly. The regular "bag of words" representation used in text ...
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