View source for 18201999. PREDICTION MODEL GENERATION APPARATUS, PREDICTION APPARATUS, PREDICTION MODEL GENERATION METHOD, PREDICTION METHOD, AND PROGRAM simplified abstract (NEC Corporation)

=PREDICTION MODEL GENERATION APPARATUS, PREDICTION APPARATUS, PREDICTION MODEL GENERATION METHOD, PREDICTION METHOD, AND PROGRAM=


==Organization Name==

[[:Category:NEC Corporation|NEC Corporation]]


[[Category:NEC Corporation]]

==Inventor(s)==

[[:Category:Eiji Yumoto of Tokyo (JP)|Eiji Yumoto of Tokyo (JP)]][[Category:Eiji Yumoto of Tokyo (JP)]]

[[:Category:Masahiro Hayashitani of Tokyo (JP)|Masahiro Hayashitani of Tokyo (JP)]][[Category:Masahiro Hayashitani of Tokyo (JP)]]

[[:Category:Kosuke Nishihara of Tokyo (JP)|Kosuke Nishihara of Tokyo (JP)]][[Category:Kosuke Nishihara of Tokyo (JP)]]

==PREDICTION MODEL GENERATION APPARATUS, PREDICTION APPARATUS, PREDICTION MODEL GENERATION METHOD, PREDICTION METHOD, AND PROGRAM - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18201999 titled 'PREDICTION MODEL GENERATION APPARATUS, PREDICTION APPARATUS, PREDICTION MODEL GENERATION METHOD, PREDICTION METHOD, AND PROGRAM

==Simplified Explanation==

The patent application describes a prediction model generation apparatus that aims to reduce the calculation load in the prediction phase while maintaining good interpretability. 

* Calculates the contribution degree of each feature to the prediction result using a test data set.
* Selects at least one feature based on the degree of contribution.
* Generates a new prediction model that outputs a prediction result based on the selected feature(s).

== Potential Applications ==
This technology could be applied in various fields such as finance, healthcare, marketing, and more where predictive modeling is used to make informed decisions.

== Problems Solved ==
1. Reducing calculation load in the prediction phase.
2. Improving the interpretability of prediction models.

== Benefits ==
1. Enhanced efficiency in prediction modeling.
2. Improved understanding of feature contributions.
3. Better decision-making based on prediction results.

== Potential Commercial Applications ==
"Enhancing Predictive Models for Improved Decision Making"

== Possible Prior Art ==
There are existing prediction model generation techniques that focus on feature selection and model interpretability, but this specific approach of calculating contribution degrees and selecting features based on them may be unique.

== Unanswered Questions ==
1. How does the apparatus handle complex datasets with a large number of features?
2. Are there any limitations to the types of prediction models that can be generated using this approach?


==Original Abstract Submitted==

In order to attain an object of generating a prediction model which not only is capable of reducing a calculation load in a prediction phase but also has a good interpretability, a prediction model generation apparatus includes: a contribution degree calculation section that calculates, with use of a test data set different from a training data set used in training of a prediction model to be tested, a degree of contribution of each of a plurality of features to a prediction result, a value of the each of the plurality of features being inputted to the prediction model to be tested; a feature selection section that selects, on the basis of the degree of contribution of the each of the plurality of features, at least one feature from among the plurality of features; and a prediction model generation section that generates a new prediction model which, upon receiving input of a value of the at least one feature selected, outputs a prediction result.



[[Category:G06N20/00]]