View source for Palantir technologies inc. (20240211106). USER INTERFACE BASED VARIABLE MACHINE MODELING simplified abstract

=USER INTERFACE BASED VARIABLE MACHINE MODELING=


==Organization Name==

[[:Category:palantir technologies inc.|palantir technologies inc.]]


[[Category:palantir technologies inc.]]

==Inventor(s)==

[[:Category:Matthew Maclean of New York NY (US)|Matthew Maclean of New York NY (US)]][[Category:Matthew Maclean of New York NY (US)]]

[[:Category:Benjamin Duffield of New York NY (US)|Benjamin Duffield of New York NY (US)]][[Category:Benjamin Duffield of New York NY (US)]]

[[:Category:Mark Elliot of London (GB)|Mark Elliot of London (GB)]][[Category:Mark Elliot of London (GB)]]

==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.



[[Category:G06F3/0482]]
[[Category:G06F3/04817]]
[[Category:G06N20/00]]
[[Category:CPC_G06F3/0482]]