fitcecoc and fitclinear are suitable for categorical data ?
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Hi,
I'm a newbie in Machine Learning and i'm trying to create a model on a kaggle dataset below.
The thing is i'd like to use k-fold crossvalidation on my model but i cannot use fifcecoc and fitclinear functions because they only accept numerical values whereas i also have some categorical ones.
Is there a way to use them with categorical data or i need to use cvpartition for k-fold crossvalidation due to the fact that my dataset not completely numerical ?
Also can i use regularization with categorical variables ?
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Jon Cherrie
2021-4-23
You should be able to use fitcecoc with categorical predictors and k-fold cross-validation. Here is an example using the "carbig" data and k=5 folds:
load carbig.mat
t = table(Acceleration,Cylinders,Displacement,Horsepower,Mfg,Model_Year,MPG,Origin,Weight);
t.Mfg = categorical(cellstr(t.Mfg));
t.Cylinders = categorical(t.Cylinders);
t.Origin = categorical(cellstr(t.Origin));
m = fitcecoc(t,"Cylinders","KFold",5)
In this cases I get warning about unique class values (which I think means I have more folds than this data will tolerate), but I can see the categorical predictors in the output:
>> m
m =
ClassificationPartitionedECOC
CrossValidatedModel: 'ECOC'
PredictorNames: {'Acceleration' 'Displacement' 'Horsepower' 'Mfg' 'Model_Year' 'MPG' 'Origin' 'Weight'}
CategoricalPredictors: [4 7]
ResponseName: 'Cylinders'
NumObservations: 406
KFold: 5
Partition: [1×1 cvpartition]
ClassNames: [3 4 5 6 8]
ScoreTransform: 'none'
If this example, doesn't work for you, can you share some code and an error message?
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