# What is Predictor? ## Predictor: Advanced Machine Learning for Material Properties Predictor is a machine learning system engineered to forecast material properties across a broad spectrum of materials including steels, non-ferrous alloys, and polymers. Utilizing extensive datasets from [Total Materia Horizon](/welcome-to-total-materia-horizon.md) curated database, the system employs proprietary methodologies for data curation, classification, and normalization to predict physical and mechanical properties with high precision. Key Features: * **High Accuracy**: Predictor achieves a relative error rate of 5% or less, ensuring precise results for engineering applications, such as CAE (Computer-Aided Engineering) and FEA (Finite Element Analysis), making it ideal for simulations and performance analysis. * **Data Gap Bridging**: One of Predictor's standout features is its ability to fill in data gaps where experimental data may be missing. By utilizing machine learning, the system provides reliable estimates for material properties that would otherwise be unavailable, significantly expanding the range of usable data. * **Advanced ML Architectures**: Incorporates cutting-edge machine learning technologies, such as XGBoost and TabNet, to manage large and complex datasets. These advanced architectures ensure high-quality predictions by efficiently handling data variability and improving model performance. * **Specialized Models**: The development of Predictor involved creating multiple, tailored machine learning models for specific material families and properties. This specialization improves the accuracy and relevance of predictions for different applications, whether you're dealing with metals, polymers, or other material types. Total Materia Predictor is designed with a user-friendly interface, enabling engineers to easily access and utilize predicted material properties in their analyses and simulations. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.totalmateria.com/predictor/user-guide/what-is-predictor.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.