Answer: Word Embeddings

The correct answer is **D. Word Embeddings**. Here's why: - **A. Tokenization**: Splits text into tokens (words, phrases) but doesn't convert them into numerical vectors. - **B. Text Encoding**: Converts text characters into numerical codes (e.g., ASCII) without capturing semantic meaning. - **C. Feature Extraction**: A broad term for techniques to extract features from data, not specifically for text-to-vector conversion. - **D. Word Embeddings (CORRECT)**: Maps words to numerical vectors in a way that similar words have similar vectors, capturing semantic relationships. Databricks MLlib supports integration with pre-trained models like Word2Vec or GloVe for this purpose. **Using Word Embeddings in Databricks MLlib**: 1. **Load Text Data**: Import your text data into a DataFrame. 2. **Preprocessing**: Tokenize and clean the text (e.g., remove stop words). 3. **Load Pre-trained Word Embeddings**: Utilize models from sources like Gensim or spaCy. 4. **Convert Words to Vectors**: Apply the model to transform words into numerical vectors. 5. **Machine Learning Model**: Use these vectors as features in your ML model. This approach effectively converts textual data into numerical features, enhancing your machine learning models' performance.

Author: LeetQuiz Editorial Team