Robert bosch gmbh (20240135160). SYSTEM AND METHOD FOR EFFICIENT ANALYZING AND COMPARING SLICE-BASED MACHINE LEARN MODELS simplified abstract
Contents
- 1 SYSTEM AND METHOD FOR EFFICIENT ANALYZING AND COMPARING SLICE-BASED MACHINE LEARN MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND METHOD FOR EFFICIENT ANALYZING AND COMPARING SLICE-BASED MACHINE LEARN MODELS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SYSTEM AND METHOD FOR EFFICIENT ANALYZING AND COMPARING SLICE-BASED MACHINE LEARN MODELS
Organization Name
Inventor(s)
Jorge Henrique Piazentin Ono of Sunnyvale CA (US)
SYSTEM AND METHOD FOR EFFICIENT ANALYZING AND COMPARING SLICE-BASED MACHINE LEARN MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135160 titled 'SYSTEM AND METHOD FOR EFFICIENT ANALYZING AND COMPARING SLICE-BASED MACHINE LEARN MODELS
Simplified Explanation
The abstract describes a computer-implemented method for a machine-learning network that involves training a shallow regressor model to predict residuals associated with a machine-learning model's predictions on slices of an input dataset, in order to optimize the model's accuracy.
- The method involves receiving an input dataset and sending it to a machine-learning model for predictions.
- Slices of the input dataset are identified and used to train a shallow regressor model to predict residuals of the machine-learning model.
- The shallow regressor is trained to compute predicted residuals of the selected slices, and an optimized model is generated based on these predictions.
- The modified effect of each slice is determined by comparing the accuracy of the optimized model with the original accuracy of the machine-learning model.
- The modified effect is output to a graphical interface for visualization.
Potential Applications
- Improving the accuracy of machine-learning models.
- Enhancing the performance of predictive analytics systems.
Problems Solved
- Addressing inaccuracies in machine-learning predictions.
- Optimizing model performance on specific subsets of data.
Benefits
- Increased accuracy of predictions.
- Better understanding of model performance on different data slices.
Potential Commercial Applications
Optimizing machine-learning models for various industries, such as finance, healthcare, and marketing.
Possible Prior Art
One possible prior art in this field is the use of ensemble learning techniques to improve the accuracy of machine-learning models. Another could be the application of residual analysis in predictive modeling to enhance model performance.
What are the potential limitations of this method in real-world applications?
One potential limitation of this method in real-world applications could be the computational resources required to train the shallow regressor model on large datasets. Additionally, the effectiveness of the optimization process may vary depending on the complexity of the data and the machine-learning model being used.
How does this method compare to existing techniques for optimizing machine-learning models?
This method differs from traditional hyperparameter tuning and cross-validation techniques by focusing on optimizing model accuracy on specific data slices rather than the entire dataset. By targeting subsets of data and predicting residuals, this approach may offer a more targeted and efficient way to improve model performance.
Original Abstract Submitted
a computer-implemented method for a machine-learning network that includes receiving an input dataset, sending the input dataset to a first machine-learning model to output predictions associated with the input data, identifying one or more slices associated with the input dataset and a first machine learning model in a first iteration, wherein each of the one or more slices include input data from the input dataset and common attributes associated with each slice; upon selecting one or more slices of the input dataset, training a shallow regressor model configured to predict residuals associated with the model, create a representation associated with a ground-truth label and a second representation associated with a model prediction associated with each sample associated with each of the one or more slices, determine residuals associated with every prediction of the first machine learning model, training the shallow regressor to compute one or more predicted residuals of the selected slices, generate an optimized model the predicted residuals, determine a modified accuracy of optimized predictions from the optimized model on each of the one or more slices of the input dataset, determine a modified effect of each of the one or more slices by utilizing a difference between the modified accuracy and an original accuracy associated with the first machine learning model, and output the modified effect to a graphical interface.
