Palantir technologies inc. (20240211106). USER INTERFACE BASED VARIABLE MACHINE MODELING simplified abstract
Contents
USER INTERFACE BASED VARIABLE MACHINE MODELING
Organization Name
Inventor(s)
Matthew Maclean of New York NY (US)
Benjamin Duffield of New York NY (US)
USER INTERFACE BASED VARIABLE MACHINE MODELING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240211106 titled 'USER INTERFACE BASED VARIABLE MACHINE MODELING
- Simplified Explanation:**
The comparative modeling system described in the patent application receives selections of a data set, a transform scheme, and one or more machine-learning algorithms. It then generates multiple models based on the selected algorithms, determines comparison metric values for these models, and presents the comparison metric values for evaluation.
- Key Features and Innovation:**
- System receives selections of data set, transform scheme, and machine-learning algorithms - Determines parameters within the machine-learning algorithms - Generates multiple models and comparison metric values for evaluation
- Potential Applications:**
- Data analysis and prediction in various industries such as finance, healthcare, and marketing - Optimization of machine-learning algorithms for improved accuracy and efficiency
- Problems Solved:**
- Streamlines the process of comparing and evaluating machine-learning models - Provides a systematic approach to model selection and optimization
- Benefits:**
- Enhances decision-making based on data analysis - Improves the performance of machine-learning algorithms - Saves time and resources in model evaluation and selection
- Commercial Applications:**
- Optimizing Machine Learning Models for Enhanced Predictive Analytics in Finance*
- Prior Art:**
Prior research in the field of machine learning and data analysis tools may provide insights into similar systems or methodologies for model comparison and evaluation.
- Frequently Updated Research:**
Stay updated on advancements in machine learning algorithms and data analysis techniques to further enhance the capabilities of the comparative modeling system.
- Questions about Comparative Modeling System:**
1. How does the system determine the comparison metric values for the generated models? 2. What are the potential limitations of the system in handling large and complex data sets?
Original Abstract Submitted
in various example embodiments, a comparative modeling system is configured to receive selections of a data set, a transform scheme, and one or more machine-learning algorithms. in response to a selection of the one or more machine-learning algorithms, the comparative modeling system determines parameters within the one or more machine-learning algorithms. the comparative modeling system generates a plurality of models for the one or more machine-learning algorithms, determines comparison metric values for the plurality of models, and causes presentation of the comparison metric values for the plurality of models.
