Intent Detection Model
- Framing the Problem (Machine Learning) Problem Type: This problem is a classic supervised learning task, specifically a classification problem. The goal is to classify user inputs into predefined intent categories based on labeled training data.
Features and Labels:
Features: The user input text (Sentences).
Labels: The intent categories (e.g., "EMI", "Order_Status", "Return_Exchange").
Approach: Using NLP preprocessing (e.g., tokenization, lemmatization) and a text classification algorithm (e.g., Naive Bayes) to build an end-to-end pipeline.
- Pros and Cons of Formulations Approach Considered:
Naive Bayes Classifier with CountVectorizer:
Pros: Simple, interpretable, efficient for text classification.
Cons: Assumes feature independence; may struggle with nuanced text.
Alternative Options:
Logistic Regression: More flexible but computationally heavier.
Deep Learning (e.g., LSTM, Transformers): Higher potential accuracy but requires more data and computation.
Preprocessing: Using spaCy for lemmatization and stopword removal improves input quality but adds preprocessing time.
- Building and Assessing the Model Model Pipeline:
Data loading, preprocessing, model training, and evaluation.
Results Interpretation: Using metrics like accuracy and the classification report to understand performance. If results show high variance between classes, adjustments might be needed.
- Justification and Improvements Why Results Make Sense: A simple model like Naive Bayes is effective for basic intent classification. Results may be consistent for straightforward datasets with distinct categories.
Potential Improvements: Switch to TF-IDF Vectorization: Reduces the weight of common words, potentially improving model generalization.
**Hyperparameter Tuning: **Adjusting the alpha parameter in Naive Bayes or exploring grid search for optimal settings.
**Model Upgrade: **Consider using LogisticRegression or tree-based algorithms (e.g., RandomForest) for better decision boundaries.
Advanced Models: Implement spaCy-based embeddings or switch to deep learning models for richer semantic understanding.
Rationale: These adjustments should help the model generalize better to unseen data and potentially handle more complex intent nuances.