17966794. SYSTEM AND METHOD FOR EFFICIENT ANALYZING AND COMPARING SLICE-BASED MACHINE LEARN MODELS simplified abstract (Robert Bosch GmbH)
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 17966794 titled 'SYSTEM AND METHOD FOR EFFICIENT ANALYZING AND COMPARING SLICE-BASED MACHINE LEARN MODELS
Simplified Explanation
The computer-implemented method described in the abstract involves using a shallow regressor model to improve the accuracy of predictions made by a machine-learning network. Here are some key points to note:
- The method involves receiving an input dataset and sending it to a machine-learning model to make predictions.
- Slices of the input dataset are identified and used to train a shallow regressor model to predict residuals associated with the original model's predictions.
- An optimized model is generated based on the predicted residuals, and the accuracy of predictions is improved on each slice of the input dataset.
- The modified effect of each slice is determined by comparing the modified accuracy with the original accuracy of the machine-learning model.
Potential Applications: - This technology could be applied in various fields such as finance, healthcare, and marketing to improve the accuracy of machine-learning predictions.
Problems Solved: - This method addresses the issue of improving the accuracy of machine-learning models by using a shallow regressor model to predict residuals and optimize predictions.
Benefits: - The technology can lead to more accurate predictions, which can result in better decision-making and improved outcomes in various applications.
Potential Commercial Applications: - "Enhancing Machine-Learning Predictions with Shallow Regressor Models" could be used in industries such as e-commerce, fraud detection, and personalized recommendations to enhance the performance of machine-learning models.
Possible Prior Art: - One possible prior art could be the use of ensemble learning techniques to improve the accuracy of machine-learning models. Another could be the use of feature engineering to enhance the performance of predictive models.
Unanswered Questions:
- How does the shallow regressor model improve the accuracy of machine-learning predictions?
The shallow regressor model is trained to predict residuals associated with the original model's predictions, which helps in fine-tuning the predictions and improving their accuracy.
- What are the potential limitations of using a shallow regressor model in this context?
One potential limitation could be the complexity of the dataset and the ability of the shallow regressor model to accurately predict residuals in all scenarios. Additionally, the computational resources required to train and optimize the model could be a limitation in certain applications.
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.
