Suppose I have iris data In the Dataset Format.
How to split the iris data Into training and testing for Machine learning?
For example,Transform the data for Classify.
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2 Answers
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12
Let's use the Titanic dataset
tit = ExampleData[{"Dataset", "Titanic"}];
Let's see it's columns
tit[Union, Keys]
Let's choose an objective
obj = "survived";
Let's add an id to each row
tit = tit[AssociationThread[Range@Length@#, #] &];
Let's create a database that splits the features and objective, to make this general.
titSplit =
tit[All, <|"Features" -> Values@*KeyDrop[obj],
"Objective" -> Key[obj]|>];
Let's split the rows arbitrarily to have a test and train set.
numTraining = 200;
ids = Range@tit[Length];
testIds = ids~RandomSample~numTraining;
trainIds = ids~Complement~testIds;
titUnclass = titSplit[<|"Test" -> testIds, "Train" -> trainIds|>];
Let's train the classifier
cfun = titUnclass["Train", Classify@*Values, #Features -> #Objective &];
Let's create a new dataset appending the classifications as a column in the Test dataset.
titClass =
titUnclass[{"Test" ->
Query[All, Append[#, <|"Classified as" -> cfun@#Features|>] &]}];
Or perhaps just have the results in a separate database results = titUnclass["Test", All, cfun@#Features &];
Let's ask for performance measures
cfm = titUnclass["Test",
ClassifierMeasurements[cfun, #] & @* Values,
#Features -> #Objective &];
cfm["Accuracy"]
(* 0.75 *)
Edit
This is based on the OP @PhilChang nice suggestion in the comments
RandomSample works on Datasets
rtit = RandomSample@ExampleData[{"Dataset", "Titanic"}];
Split
obj = "survived"; numTrain = 200;
ctit = rtit[<|"Train" -> (;; numTrain), "Test" -> (numTrain + 1 ;;)|>];
Train
cfun = ctit["Train", GroupBy[Key[obj] -> KeyDrop[obj]], Values][
Classify];
Measure
cfm = ctit["Test", GroupBy[Key[obj] -> KeyDrop[obj]], Values][
ClassifierMeasurements[cfun, #] &];
cfm["Accuracy"]
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2$\begingroup$ I get an
Accuracy= 0.63. Naive splitting like this is often biased by the dataset layout, eg, look at the distribution of "class":trainTit[Map[First] /* Counts, "Features"]-->"1st" -> 200versustestTit[Map[First]...]-->"1st" -> 123, "2nd" -> 277, "3rd" -> 709. Typically, bootstrap or other resampling methods are used to minimize the effects of clustering. Certainly you'd want to randomize the partition. $\endgroup$ Commented Aug 12, 2014 at 19:49 -
8$\begingroup$ We need to make this easier, probably with a new function. Especially making sure you preserve the frequencies of whatever classes when you do the split. $\endgroup$ Commented Aug 12, 2014 at 20:30
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1$\begingroup$ Thank you all,I learned a lot from it. For this moment the best way maybe: 1. Random the dataset, randsample = RandomSample @ titanic; 2. get train and test data. train = RandSample[;;3000];test = randomSample[3001;;]; 3. transform the data set for classify use. train = titanic[GroupBy[Key[obj]], All, Values@*Normal@*KeyDrop[obj]]; $\endgroup$ Commented Aug 13, 2014 at 6:31
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3$\begingroup$ If Only Classify accept a Dataset as its argument:Classify[mydataset,y,{x1,x2,...,xp}] $\endgroup$ Commented Aug 13, 2014 at 6:36
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1$\begingroup$ @GordonCoale You can use the
ValidationSetoption toClassifyandPredictto override our internal cross-validation if you have your own test set. Also, @Rojo, note that in 10.0.2 you can use theClassify[data -> out]shorthand to indicate that the column name or number is the one being predicted, so you don't have to split off the features from the output yourself. $\endgroup$ Commented Feb 9, 2015 at 18:50
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I think there is a resource function now...
https://resources.wolframcloud.com/FunctionRepository/resources/TrainTestSplit/
