Techniques for creating new features and enhancing model input.
Why it's important: Feature engineering is essential before applying machine learning models because it helps extract meaningful insights from raw data. By creating and selecting the right features, you can improve model accuracy, reduce overfitting, and enhance the model's ability to generalize to unseen data.
Custom and standard transformers to scale, normalize, and prepare data.
Why it's important: Data transformers are crucial for preparing data in a format suitable for machine learning algorithms. Scaling and normalizing data ensure that no single feature dominates the training process, leading to faster convergence and better model performance.
Methods to analyze cause-effect relationships within the data.
Why it's important: Understanding causal relationships helps in building models that reflect true cause and effect, rather than mere correlations. This leads to more reliable predictions and insights, which are critical for decision-making and policy development.
A combination of retrieval mechanisms and generation for improved predictions and outcomes.
Why it's important: RAG enhances machine learning models by integrating external knowledge during prediction. This approach improves the accuracy and relevance of generated outputs, especially in tasks like natural language understanding and question answering, where context is key.