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1 vote

How an I improve my prediction of my model much more than that?

The first thing I can notice is that your followers feature ranges from $0$ to $10^6$. You would want to first make your regression on a logarithmic scale: ...
Yann's user avatar
  • 11
1 vote

How to weight loss in regression

To handle imbalanced data in regression tasks, use the DenseWeights package. It assigns weights to data points using kernel density estimation (KDE) based on the ...
Kevin Patel's user avatar
0 votes

Data splitting for OLS regression

It is not clear the ratio that you have used to do the train-test split. If the dataset is small, you can try with cross-validation and check the metrics. Also, can try with various rations 80-20, 30-...
ENAT's user avatar
  • 1
1 vote

How many ways are there to check model overfitting?

Evaluating training scores—such as accuracy for classification and (adjusted) R-squared for regression—against test scores can indicate potential overfitting. However, this comparison alone doesn't ...
Seyit Hocuk's user avatar
1 vote

How do I give weight to recent time points when predicting another closeby time point?

I think the core idea of neural networks is that they find these weights by themselves. If you want the model to have certain properties with these weights, you should start with a statistical model ...
Tomasz Witkowski's user avatar
0 votes

Build a Neural Network for Multi-output Regression

I see four potential solutions for you: think through your data - are all necessary input variable included? is output in the most convenient form to model? Maybe it should be in radians and not ...
Tomasz Witkowski's user avatar

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