I need to utilize TensorFlow for a project to classify items based on their attributes to a certain class (either 1, 2, or 3).
Only problem is almost every TF tutorial or example... moreI need to utilize TensorFlow for a project to classify items based on their attributes to a certain class (either 1, 2, or 3).
Only problem is almost every TF tutorial or example I find online is about image recognition or text classification. I can't find anything about classification based on numbers. I guess what I'm asking for is where to get started. If anyone knows of a relevant example, or if I'm just thinking about this completely wrong.
We are given the 13 attributes for each item, and need to use the TF neural network to classify each item correctly (or mark the margin of error). But nothing online is showing me even how to start with this kind of dataset.
Example of dataset: (first value is class, other values are attributes)
2, 11.84, 2.89, 2.23, 18, 112, 1.72, 1.32, 0.43, 0.95, 2.65, 0.96, 2.52, 500
3, 13.69, 3.26, 2.54, 20, 107, 1.83, 0.56, 0.5, 0.8, 5.88, 0.96, 1.82, 680
3, 13.84, 4.12, 2.38, 19.5, 89, 1.8, 0.83, 0.48, 1.56, 9.01, 0.57, 1.64, 480
2, 11.56, 2.05, 3.23, 28.5, 119,... less
This question came to my mind while working on 2 projects in AI and ML. What If I'm building a model (e.g. Classification Neural Network,K-NN, .. etc) and this model uses some... moreThis question came to my mind while working on 2 projects in AI and ML. What If I'm building a model (e.g. Classification Neural Network,K-NN, .. etc) and this model uses some function that includes randomness. If I don't fix the seed, then I'm going to get different accuracy results every time I run the algorithm on the same training data. However, If I fix it then some other setting might give better results.
Is averaging a set of accuracies enough to say that the accuracy of this model is xx % ?
I'm not sure If this is the right place to ask such a question/open such a discussion. less
ANN (Artificial Neural Networks) and SVM (Support Vector Machines) are two popular strategies for supervised machine learning and classification. It's not often clear which method... moreANN (Artificial Neural Networks) and SVM (Support Vector Machines) are two popular strategies for supervised machine learning and classification. It's not often clear which method is better for a particular project, and I'm certain the answer is always "it depends." Often, a combination of both along with Bayesian classification is used.
These questions on Stackoverflow have already been asked regarding ANN vs SVM:
ANN and SVM classification
what the difference among ANN, SVM and KNN in my classification question
Support Vector Machine or Artificial Neural Network for text processing?
In this question, I'd like to know specifically what aspects of an ANN (specifically, a Multilayer Perceptron) might make it desirable to use over an SVM? The reason I ask is because it's easy to answer the opposite question: Support Vector Machines are often superior to ANNs because they avoid two major weaknesses of ANNs:
(1) ANNs often converge on local minima rather than global minima, meaning that they are... less
How do I save a trained Naive Bayes classifier to disk and use it... moreHow do I save a trained Naive Bayes classifier to disk and use it to predict data?
I have the following sample program from the scikit-learn website:
from sklearn import datasets
iris = datasets.load_iris()
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
y_pred = gnb.fit(iris.data, iris.target).predict(iris.data)
print "Number of mislabeled points : %d" % (iris.target != y_pred).sum()
I've trained a tree model with R caret. I'm now trying to generate a confusion matrix and keep getting the following error:
Error in confusionMatrix.default(predictionsTree,... moreI've trained a tree model with R caret. I'm now trying to generate a confusion matrix and keep getting the following error:
Error in confusionMatrix.default(predictionsTree, testdata$catgeory) : the data and reference factors must have the same number of levels
prob <- 0.5 #Specify class split
singleSplit <- createDataPartition(modellingData2$category, p=prob,
times=1, list=FALSE)
cvControl <- trainControl(method="repeatedcv", number=10, repeats=5)
traindata <- modellingData2
testdata <- modellingData2
treeFit <- train(traindata$category~., data=traindata,
trControl=cvControl, method="rpart", tuneLength=10)
predictionsTree <- predict(treeFit, testdata)
confusionMatrix(predictionsTree, testdata$catgeory)
The error occurs when generating the confusion matrix. The levels are the same on both objects. I cant figure out what the problem is. Their structure and levels are given below. They should be the same. Any help would be greatly appreciated as its... less
There doesn't seem to be too many options for deploying predictive models in production which is surprising given the explosion in Big Data.
I understand that the open-source PMML... moreThere doesn't seem to be too many options for deploying predictive models in production which is surprising given the explosion in Big Data.
I understand that the open-source PMML can be used to export models as an XML specification. This can then be used for in-database scoring/prediction. However it seems that to make this work you need to use the PMML plugin by Zementis which means the solution is not truly open source. Is there an easier open way to map PMML to SQL for scoring?
Another option would be to use JSON instead of XML to output model predictions. But in this case, where would the R model sit? I'm assuming it would always need to be mapped to SQL...unless the R model could sit on the same server as the data and then run against that incoming data using an R script?
Any other options out there? less
I am trying to apply deep learning for a binary classification problem with high class imbalance between target classes (500k, 31K). I want to write a custom loss function which... moreI am trying to apply deep learning for a binary classification problem with high class imbalance between target classes (500k, 31K). I want to write a custom loss function which should be like: minimize(100-((predicted_smallerclass)/(total_smallerclass))*100)
Appreciate any pointers on how I can build this logic.
I am writing my own code for a decision tree. I need to decide on when to terminate the tree building process. I could think of limiting the height of the tree, but this seems... moreI am writing my own code for a decision tree. I need to decide on when to terminate the tree building process. I could think of limiting the height of the tree, but this seems trivial. Could anyone give me a better idea on how to implement my termination function.
Here in my tree building algorithm.