Crashes of Classify function

I'm working on a classifying two classes. I am training my Classify function with the RandomForest method, but it crashes. The size of training data is 2365819 examples as in the following code.

c = Classify[trainingData, Method -> "RandomForest"];

trainingData = {{{0.2443901294459203, 0.6339894544265186, 0.348376166232641,
0.6205766754715589, 0.6949430713884377, 0.09981477603065425,
0.2652380125320035, 0.7326739707331305, 0.33572175175325736,
0.15215974016395206}, {0.40877022570675847, 0.5013459204618054,
0.18091097996572647, 0.9841680784952942, 0.46040777479106754,
0.9721054020948912, 0.7980120864921296, 0.985753996194227,
0.36559491598469984, 0.2578547049404678}} -> 0.0
{{0.033860045146726865, 110., 3., 0., 0., 0.,
0.13636363636363635, 0.9766334638696152, 3.,
1972.}, {0.035211267605633804, 177., 8., 0., 0., 0.,
0.0903954802259887, 0.9766334638696152, 3., 1145.}} -> 1.}


The link to the training data is data. Any suggestion on how to run classification without crashing?

Note: I am using version 11.3 on Win.

• You are missing a comma, and Classify expects integer targets, otherwuse use Predict. – M.R. Oct 9 '18 at 19:43
• Hi @M.R. yes, it is my typo with the comma. Unfortunately, I have to disagree with you. It is written, in the description of the function that "Classify can be used on many types of data, including numerical...". Additionally, when I take a small training set, the Classify run correctly. – Kiril Danilchenko Oct 9 '18 at 20:20
• ok then post your full set with CloudPut or Dropbox and we can try to help you. – M.R. Oct 9 '18 at 20:40
• @M.R. I edited the question – Kiril Danilchenko Oct 10 '18 at 6:05
• Do you know offhand what if anything correlates with the crash? Are you doing other things at the time, either with kernels or the UI? – Daniel Lichtblau Oct 19 '18 at 14:51

I turned off the progress reporting of the classify function. the classification process finished running without crash

c = Classify[trainingData, Method -> "RandomForest", TrainingProgressReporting -> None
];

trainingData = {{{0.2443901294459203, 0.6339894544265186,
0.348376166232641, 0.6205766754715589, 0.6949430713884377,
0.09981477603065425, 0.2652380125320035, 0.7326739707331305,
0.33572175175325736,
0.15215974016395206}, {0.40877022570675847,
0.5013459204618054, 0.18091097996572647, 0.9841680784952942,
0.46040777479106754, 0.9721054020948912, 0.7980120864921296,
0.985753996194227, 0.36559491598469984,
0.2578547049404678}} ->
0 , {{0.033860045146726865, 110., 3., 0., 0., 0.,
0.13636363636363635, 0.9766334638696152, 3.,
1972.}, {0.035211267605633804, 177., 8., 0., 0., 0.,
0.0903954802259887, 0.9766334638696152, 3., 1145.}} -> 1};

c = Classify[trainingData, Method -> "RandomForest"]
`