文档分类预测报错AttributeError: lower not found及准确率提升咨询

76% is a solid baseline, but there's plenty of room to improve. Here are actionable steps prioritized by impact:

• Refine text preprocessing:
  • Add targeted cleaning: Remove HTML tags, special characters, or domain-specific noise (e.g., code snippets if you're classifying technical docs). For English, try spelling correction or lemmatization/stemming to normalize words.
  • Tune your stopword list: Don't rely solely on generic stopwords—add domain-specific terms that don't add predictive value (e.g., "report" for financial document classification).
  • Experiment with different tokenizers: Tools like spaCy (for English) or jieba (for Chinese) often capture context better than basic split methods.
• Upgrade your model/feature extraction:
  • If you're using traditional methods (TF-IDF + SVM/Naive Bayes), switch to pre-trained language models like BERT or RoBERTa. Libraries like Hugging Face Transformers make this straightforward, and these models usually deliver big accuracy gains on text tasks.
  • Adjust TF-IDF parameters: Try ngram_range=(1,2) to include bigram features, or tweak min_df/max_df to filter out overly rare or common words that don't help with classification.
• Improve your dataset:
  • Fix class imbalance: If some classes have way fewer samples, use techniques like oversampling (SMOTE for text), undersampling, or set class_weight='balanced' in your model (if supported).
  • Augment your data: Generate synthetic samples by replacing words with synonyms, random insertion/deletion, or back-translation (e.g., translate English to French and back) to expand your training set.
• Tune hyperparameters:
  • Use GridSearchCV or RandomizedSearchCV to optimize model parameters—for example, the C value in SVM, alpha in Naive Bayes, or learning rate/batch size for neural models.
• Analyze errors:
  • Dig into misclassified samples to spot patterns: Are confusing classes semantically similar? Is preprocessing stripping out critical information? Use these insights to adjust your approach (e.g., adding class-specific features).

内容的提问来源于stack exchange，提问作者Madhi