# Viewing features feature_names = vectorizer.get_feature_names_out() print("Features:", feature_names) print("TF-IDF Features:", tfidf_features.toarray()) This example uses CountVectorizer and TfidfTransformer from scikit-learn to create basic features from your text. Adjustments would be needed based on your specific use case and data.
# Your data text = "in3x,net,watch,14zwhrd6,dildo,18" in3x,net,watch,14zwhrd6,dildo,18
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer # Viewing features feature_names = vectorizer
# Tokenize (simple split) tokens = text.split(',') feature_names) print("TF-IDF Features:"
# TF-IDF transformer tfidf = TfidfTransformer() tfidf_features = tfidf.fit_transform(count_features)