When the sklearn model is referenced and the model is evaluated after fit, the error occurs. The code is as follows:

from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import f1_score

regressor = DecisionTreeClassifier(random_state=42)
regressor.fit(X_train,y_train)
y_pred = regressor.predict(X_test)

print(f1_score(y_pred,y_test))

After running, it appears:

target is multiclass but average='binary'. please choose another average setting.

In fact, there is no problem with the code. The problem lies in the data type, y here_ The values in test are not binary of 0 and 1, but [123, 23142243 ]Therefore, in essence, it is impossible to carry out hair care_ Score calculation, you will find that if F1_ Change score to accuracy_ Score, the return result is 0
so to predict y of this kind of value type, other evaluation indicators, such as R 2, are needed. The code is as follows:

from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import r2_score
regressor = DecisionTreeClassifier(random_state=42)
regressor.fit(X_train,y_train)

# TODO: output predicted scores on the test set
y_pred = regressor.predict(X_test)

print(r2_score(y_pred,y_test))

result:

0.12800402751210926