I'm trying to build a graph neural network to solve a tabular binary classification problem ( same as heart desease prediction ) but unfortunately I couldn't find any source or explanation about it all what I could find was super advance that I couldn't understand anything from it.
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I used Caret to create a regression model of a dataset in R, and I wish to find this equation for usage in other websites (e.g. Desmos). I am unable to find info anywhere on how to do this, so if anyone has answers, that would be much appreciated! :D
Hello stackoverflow community,
Im working on a uni-project in which we try to create Bayesian Network Classifier from data in R.
Ideally the classifier should be based on a General Bayesian Network (GNB) or a BN Augmented Naive Bayes(BAN).
Unfortunately Im yet to find a suitabel package to create either of those nets in R.
My research led me to the following two packages:
bnclassify, the most prominent package for BN classification, doesnt include GNBs or BANs at all.
bnlearn offers the possibility to learn GNBs but according to the creator the learning is focused on returning the correct dependence structure rather than maximizing the predictive accuracy for classification. I've tried to use them for my classification problem nonetheless but the result was underwhelming.
So my question is if anyone knows a R package to classify with GNBs or BANs
OR how to work with the GNBs fron bnlearn to improve their predictive accuracy for classification problems.
Thanks you for your help in advance.
Best Regards
I'm dealing with an image classification problem in which I have different types of labels for every observation. I'm trying to do multitask learning using R keras. The only problem is that I have to use the keras 'Data Loader' because the dataset is huge and I don't know how to use the flow_images_from_directory function for multi-output models. Please someone help me.
is there any way I can display an image or diagram of my neural net using h20 in R. Also, I went through the h20 documentation but couldn't figure out to extract weights from the neural net object.
In h2o.deeplearning() set export_weights_and_biases=T and then once your model has finished building you can extract the weights with h2o.weights(). H2O doesn't provide methods to display a diagram for your neural net.
I have a dataset, downloaded from link.
I know about coremltool (created by Apple).
The question is:
is it possible to convert Stanford dataset to CoreML ?
If yes - can somebody give me instructions ?
Thanks in advance !
This question is asked so often, that finally I've decided to draw a diagram.
Explanation:
Dataset is a "fuel" that you put into your model to make it work.
Model is a machine learning algorithm: neural network, decision tree etc.
Supported ML frameworks and models are listed here together with the instructions for conversion.
You can make your own .mlmodel file using your own data set with a python script and a python library called coremltools. You can train your model using sklearn, keras, etc. and can customize what is uses to train like SVM, kNN, regression, and so on. Then you save it as a .mlmodelfile and drop that into your project. This video is helpful:
https://youtu.be/T4t73CXB7CU