Review this generated ticket-classification training function.
Train from rows containing text, label, and conversation_id without leaking conversations; preserve source text and report safe diagnostic metrics.
Python
import re
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
def train_and_report(rows):
texts = [row["text"] for row in rows]
labels = [row["label"] for row in rows]
normalized = [re.sub(r"[^\w\s]", "", text).lower() for text in texts]
vectorizer = TfidfVectorizer()
features = vectorizer.fit_transform(normalized)
train_x, test_x, train_y, test_y = train_test_split(
features, labels, test_size=0.2, random_state=42
)
model = LogisticRegression().fit(train_x, train_y)
predictions = model.predict(test_x)
print("accuracy", accuracy_score(test_y, predictions))
print(list(zip(texts, predictions)))
return model, vectorizer
generated code is illustrative, not from any one model