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I want to train a model using RandomForestClassifier but there are infinite values in my data
How can I handle infinite values in the pipeline before the algorithm?
Can anyone help me?
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Can you help me solve this please
[enter image description here[[[[enter image description here](https://i.stack.imgur.com/utguF.jpg)](https://i.stack.imgur.com/dd6gE.jpg)](https://i.stack.imgur.com/nf3KN.jpg)](https://i.stack.imgur.com/1nfxb.jpg)](https://i.stack.imgur.com/ebSIl.jpg)
I tried everything..
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Is it doable to update my trained model (fbprophet) in order to surpass the dilemma of retraining the whole dataset every time?
Thanks for your help in Advance
I found a solution using the warm-start approach, here.
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I'm preparing my dataset to be preprocessed before training with CNN model but i couldn't generate data from this type of file which contain several signals.
I recommend using the gdflib library. It'll allow you to process your .gdf files by organizing your data into nodes for further processing.
It would also help if you could please provide a minimal reproducible example of what you have tried.
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the corresponding pattern is ('Handmade Frozen Car', 11.0). I want to get the value as 11.0. Any suggestions are highly appreciated.
x = ('Handmade Frozen Car', 11.0)
secondElement = x[1]
secondElement will be equal to 11.0
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How would I use the dataset at http://oceancolor.gsfc.nasa.gov/DOCS/DistFromCoast/ to efficiently determine the distance of a given coordinate (lat,lng) to the nearest coastline?
It's quite a large file. Is there a library that can help with processing this kind of data?