This code does not work and gives the following error: ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted']. Fix it! from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(data, data_class, random_state=42) from sklearn.discriminant_analysis import LinearDiscriminantAnalysis lda.fit(X_train, y_train) from sklearn.metrics import f1_score y_pred = lda.predict(X_test) f1_score(y_test, y_pred) Write code to evaluate its quality with F1 measure

from sklearn.model_selection import train_test_split
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.metrics import f1_score
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report

le = LabelEncoder()
le.fit(data_class)
data_class = le.transform(data_class)

X_train, X_test, y_train, y_test = train_test_split(data, data_class, random_state=42)

lda = LinearDiscriminantAnalysis()
lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)

f1 = f1_score(y_test, y_pred, average='macro') # average='macro'
print('F1:', f1)

#confusion_matrix(y_test, y_pred)
print(classification_report(y_test, y_