This article details seven advanced feature engineering techniques using LLM embeddings to improve machine learning model performance. It covers techniques like dimensionality reduction, semantic similarity, clustering, and more.
The article explores how to leverage LLM embeddings for advanced feature engineering in machine learning, going beyond simple similarity searches. It details seven techniques:
1. **Embedding Arithmetic:** Performing mathematical operations (addition, subtraction) on embeddings to represent concepts like "positive sentiment - negative sentiment = overall sentiment".
2. **Embedding Clustering:** Using clustering algorithms (like k-means) on embeddings to create categorical features representing groups of similar text.
3. **Embedding Dimensionality Reduction:** Reducing the dimensionality of embeddings using techniques like PCA or UMAP to create more compact features while preserving important information.
4. **Embedding as Input to Tree-Based Models:** Directly using embedding vectors as features in tree-based models like Random Forests or Gradient Boosting. The article highlights the importance of careful handling of high-dimensional data.
5. **Embedding-Weighted Averaging:** Calculating weighted averages of embeddings based on relevance scores (e.g., TF-IDF) to create a single, representative embedding for a document.
6. **Embedding Difference:** Calculating the difference between embeddings to capture changes or relationships between texts (e.g., before/after edits, question/answer pairs).
7. **Embedding Concatenation:** Combining multiple embeddings (e.g., title and body of a document) to create a richer feature representation.
The article explores how to leverage LLM embeddings for advanced feature engineering in machine learning, going beyond simple similarity searches. It details seven techniques:
1. **Embedding Arithmetic:** Performing mathematical operations (addition, subtraction) on embeddings to represent concepts like "positive sentiment - negative sentiment = overall sentiment".
2. **Embedding Clustering:** Using clustering algorithms (like k-means) on embeddings to create categorical features representing groups of similar text.
3. **Embedding Dimensionality Reduction:** Reducing the dimensionality of embeddings using techniques like PCA or UMAP to create more compact features while preserving important information.
4. **Embedding as Input to Tree-Based Models:** Directly using embedding vectors as features in tree-based models like Random Forests or Gradient Boosting. The article highlights the importance of careful handling of high-dimensional data.
5. **Embedding-Weighted Averaging:** Calculating weighted averages of embeddings based on relevance scores (e.g., TF-IDF) to create a single, representative embedding for a document.
6. **Embedding Difference:** Calculating the difference between embeddings to capture changes or relationships between texts (e.g., before/after edits, question/answer pairs).
7. **Embedding Concatenation:** Combining multiple embeddings (e.g., title and body of a document) to create a richer feature representation.
PCA (principal component analysis) can be effectively used for outlier detection by transforming data into a space where outliers are more easily identifiable due to the reduction in dimensionality and reshaping of data patterns.
Exploratory data analysis (EDA) is a powerful technique to understand the structure of word embeddings, the basis of large language models. In this article, we'll apply EDA to GloVe word embeddings and find some interesting insights.
This article explains the PCA algorithm and its implementation in Python. It covers key concepts such as Dimensionality Reduction, eigenvectors, and eigenvalues. The tutorial aims to provide a solid understanding of the algorithm's inner workings and its application for dealing with high-dimensional data and the curse of dimensionality.
Alternative to t-SNE and PCA
