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from sklearn.linear_model import LogisticRegression

from sklearn.metrics import mean_squared_log_error

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import mean_squared_log_error
logreg = LogisticRegression(random_state=0, solver='liblinear').fit(X_train, y_train)
y_pred = logreg.predict(X_test)
mean_squared_log_error(y_test, y_pred)