I want to build an autoencoder model with the Caret package with the following features:
1) Build an unsupervised neural network model using deep learning autoencoders
2) Using the autoencoder model in (1) as a pre-training input for a supervised model.
Online examples on using autoencoder in caret are quite few and far in between, offering no real insight into practical use cases.
I'm under data privacy and resource constraints so I'm unable to use H2o or Keras for neural networks.
Sample data for the model can be found at:
https://www.kaggle.com/nodarokroshiashvili/credit-card-fraud/data
An example of this in H2o is at this link:
https://shiring.github.io/machine_learning/2017/05/01/fraud
Any help or pointers in the right direction in this regard will be appreciated.
EDIT:
Thanks to Lauren and Erin, staff at H20 commenting that data privacy should not be a concern because H20 creates a cluster which is located on premise and not in an 'H20.cloud'
Related
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 trying to build a random forest using model based regression trees in partykit package. I have built a model based tree using mob() function with a user defined fit() function which returns an object at the terminal node.
In partykit there is cforest() which uses only ctree() type trees. I want to know if it is possible to modify cforest() or write a new function which builds random forests from model based trees which returns objects at the terminal node. I want to use the objects in the terminal node for predictions. Any help is much appreciated. Thank you in advance.
Edit: The tree I have built is similar to the one here -> https://stackoverflow.com/a/37059827/14168775
How do I build a random forest using a tree similar to the one in above answer?
At the moment, there is no canned solution for general model-based forests using mob() although most of the building blocks are available. However, we are currently reimplementing the backend of mob() so that we can leverage the infrastructure underlying cforest() more easily. Also, mob() is quite a bit slower than ctree() which is somewhat inconvenient in learning forests.
The best alternative, currently, is to use cforest() with a custom ytrafo. These can also accomodate model-based transformations, very much like the scores in mob(). In fact, in many situations ctree() and mob() yield very similar results when provided with the same score function as the transformation.
A worked example is available in this conference presentation:
Heidi Seibold, Achim Zeileis, Torsten Hothorn (2017).
"Individual Treatment Effect Prediction Using Model-Based Random Forests."
Presented at Workshop "Psychoco 2017 - International Workshop on Psychometric Computing",
WU Wirtschaftsuniversität Wien, Austria.
URL https://eeecon.uibk.ac.at/~zeileis/papers/Psychoco-2017.pdf
The special case of model-based random forests for individual treatment effect prediction was also implemented in a dedicated package model4you that uses the approach from the presentation above and is available from CRAN. See also:
Heidi Seibold, Achim Zeileis, Torsten Hothorn (2019).
"model4you: An R Package for Personalised Treatment Effect Estimation."
Journal of Open Research Software, 7(17), 1-6.
doi:10.5334/jors.219
I tried to build a pre-trained core-ml model with the help of create ML framework, but the model created is not updatable, Is there a way to create a pre-trained core-ml model which can be updated on the device itself (newly introduced feature in Core-ML 3) ?
Not directly with Create ML, you'll have to use coremltools to make the model updatable. See here for examples: https://github.com/apple/coremltools/tree/main/examples
However... this will only work for neural networks and k-nearest neighbors models. Create ML does not actually produce these kinds of models (at the moment).
For example, an image classifier trained with Create ML is a GLM on top of a fixed neural network. You cannot make GLM models updatable at this point.
So in short, no, you can't make models trained with Create ML updatable.
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