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Assuming that I have an excel sheet with three columns of data and one column of label. Can I use all three columns of data to train a neural network to predict the label?
Definitely. Here is a sketch of such a neural network:
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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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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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I am working on pattern recognition program using R/python. What would be the best way to compare two or more figures and identify/recognize the similar or duplicate figures based on pattern recognition?
There are lots of papers on the internet, we can try to get the idea how to extract and process feature in a fingerprint. For instance, http://www.cse.unr.edu/~bebis/CS790Q/PaperPresentations/MinutiaeDetection.pdf
Then you can use whatever classifier you want such as support vector machine.
If you need more idea you can visit http://dermatoglyphics.org/11-basic-patterns-of-fingerprint/ to generalize
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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?