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Open source Anomaly Detection in Python
17 votes

h2o has an anomaly detection module and traditionally the code is available in R.However beyond version 3 it has similar module available in python as well,and since h2o is open source it might fit ...

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Does modeling with Random Forests require cross-validation?
8 votes

By default random forest picks up 2/3rd data for training and rest for testing for regression and almost 70% data for training and rest for testing during classification.By principle since it ...

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How to avoid overfitting in random forest?
4 votes

Here is a nice link on that on stackexchange, however my general experience is the more depth the model has the more it tends to overfit.

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Random forest model gives same result for all test data, Next step?
2 votes

Some of the possibilities include the following: 1) The training data has class imbalance. Solution: Train the model using CV = 5 or 10; Do a log transformation to make the target distribution more ...

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How to do this complicated data extrapolation, prediction modeling?
2 votes

I am not sure I understood the problem,however if you are trying to predict sales amount my guess is ARIMA might not be the right choice as it will not consider external variables.My suggestion is to ...

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Bigdata cluster compatible distributed predictive model
1 votes

While checking on python xgboost I found the existence of this open source project that helps create scalable machine learning program.Should be worth exploring.

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Which cross-validation type best suits to binary classification problem
0 votes

To be honest binary classification is the easiest type compared to multi-class classification as at times by error you can classify a wrong class to a right one.So if you have a dataset with ...

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Scalable Outlier/Anomaly Detection
0 votes

You can refer to my response related to h2o R or Python anomaly detection method in stackexchange,since that is scalable too.

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Adjusting Probabilities When Using Alternative Cutoffs For Classification
0 votes

I believe you can try with range of cut offs to see which one gives highest accuracy or F-score in hold out set.I've seen winners in kaggle stating to do experiment with cut-offs to get best result.

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any reason for this project to use hadoop/spark?
0 votes

Actually deep learning can be run in spark using h2o sparkling water feature.Also you can use h2o.deeplearning to run deeplearning on your data in cluster or single node.Spark is good for munging the ...

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