R package to tune parameters for machine learning(Support Vector Machine, Random Forest, and Xgboost), using bayesian optimization with gaussian process
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Error in table(testlabel, t.pred$predictions) : all arguments must have the same length #56
Hi,
I tried to run rf_opt where my train has dim=101673, 10 whilst my test dim=43574, 10.
I face this error:
Error in table(testlabel, t.pred$predictions) :
all arguments must have the same length
Timing stopped at: 4.31 0.22 1.81
I've checked there's no NA values on both dataset.
All my variables are factor.
When i run iris_train and iris_test it run smoothly. when i use iris_train as is and sampled the iris_test to 30 it still work.
when I sample my train and test both to 1000 rf_opt run smoothly.
So I'm kind of confussed here. If it because of different number of rows why on iris dataset with different number of rows it still work.
Hi, I tried to run rf_opt where my train has dim=101673, 10 whilst my test dim=43574, 10. I face this error: Error in table(testlabel, t.pred$predictions) : all arguments must have the same length Timing stopped at: 4.31 0.22 1.81
I've checked there's no NA values on both dataset. All my variables are factor.
When i run iris_train and iris_test it run smoothly. when i use iris_train as is and sampled the iris_test to 30 it still work. when I sample my train and test both to 1000 rf_opt run smoothly.
So I'm kind of confussed here. If it because of different number of rows why on iris dataset with different number of rows it still work.
thx.