Invalid classes inferred from unique values of `y`. Expected: [0 1 2 3 4 5], got [1 2 3 4 5 6]

I've trained dataset using XGB Classifier, but I got this error in local. It worked on Colab and also my friends don't have any problem with same code. I don't know what that error means...

Invalid classes inferred from unique values of y. Expected: [0 1 2 3 4 5], got [1 2 3 4 5 6]

this is my code, but I guess it's not the reason.

start_time = time.time() xgb = XGBClassifier(n_estimators = 400, learning_rate = 0.1, max_depth = 3) xgb.fit(X_train.values, y_train) print('Fit time : ', time.time() - start_time) 

8 Answers

That happens because the class column has to start from 0 (as required since version 1.3.2). An easy way to solve that is using LabelEncoder from sklearn.preprocssing library.

Solution (works for version 1.6):

from sklearn.preprocessing import LabelEncoder le = LabelEncoder() y_train = le.fit_transform(y_train) 

And then you try/run your code again:

start_time = time.time() xgb = XGBClassifier(n_estimators = 400, learning_rate = 0.1, max_depth = 3) xgb.fit(X_train.values, y_train) print('Fit time : ', time.time() - start_time) 

It's because the y_train must be encoded in a newer update XGBoost model before training it, i.e., you must use some categorical transformation like label encoders:

from sklearn.preprocessing import LabelEncoder le = LabelEncoder() y_train = le.fit_transform(y_train) 

Then apply it to XGBoost model for training:

from xgboost import XGBClassifier classifier = XGBClassifier() classifier.fit(X = X_train,y = y_train) 

After training to find out its confusion matrix you must inverse transform the predicted y values, as shown:

from sklearn.metrics import confusion_matrix, accuracy_score y_pred = classifier.predict(X_test) y_pred = le.inverse_transform(y_pred) cm = confusion_matrix(y_test, y_pred) print(cm) accuracy_score(y_test, y_pred) 

Try to adding stratify to the train_test_split code:

X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=test_size, stratify = labels) 

The erros comes with the new version of xgboost, Uninstall current Xgboost and install xgboost 0.90

pip uninstall xgboost pip install xgboost==0.90 

Downgrading to 1.5.0 worked for me

Also got this warning message during execution

UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release.

using the Label encoder in 1.6 returns this error for me:

MultiClassEvaluation: label must be in [0, num_class), num_class=6 but found 6 in label

If it helps, i just rolled back to version 1.2.1

Use python version 3.7 as used in colab

it happens because the version of ur xgboost , so :

try this :

y_train_xgb = y_train.map({"1": 0, "2": 1, "3": 2}