Cross Validation, Data Science
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I have written this code and getting error:
from sklearn.model_selection import train_test_split
X0 = pd.read_csv('C:/Users/Desktop/Dataset for Experiment - 2019/ANFIS modeling data/china_data.csv')
train_test_split. X0 = X0.drop(columns = ['EFFORT'], axis = 1)
X_train, X_test, y_train, y_test = train_test_split(X0, test_size=0.8)
**the error which is showing for this code "X_train, X_test, y_train, y_test = train_test_split(X0, test_size=0.8)
ValueError: not enough values to unpack (expected 4, got 2)"**
I tried many times to remove this error but it is still persistent. Can anybody explain to me why I am getting this error & how can I remove this error?
采纳的回答
Hiro Yoshino
2019-12-26
In MATLAB, you can use cvpartition
Train vs Test
pt = cvpartition(Y, 'HoldOut', 0.2);
trainIndex = training(pt);
testIndex = test(pt);
X_train = X(:,trainIndex);
Y_train = Y(trainIndex);
X_test = X(:,testIndex);
Y_test = Y(testIndex);
Cross validation
pt = cvpartition(Y, 'KFold', 5);
...
...
It is easier to control the data index for validation.
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