Viewing Feedbacks and Accepted Predictions

Oct 13, 2025 by magicalceylontours.com 54 views

Feature: Enhancing Model Evaluation with Feedbacks and Accepted Predictions

In the realm of machine learning, model evaluation is a crucial step in determining the performance and accuracy of a model. However, traditional evaluation methods often focus on metrics such as accuracy, precision, and recall, which may not provide a comprehensive understanding of a model's strengths and weaknesses. To address this limitation, we are introducing a new feature that enables users to view feedbacks and accepted predictions for a model, providing a more nuanced and informative evaluation experience.

Viewing Feedbacks and Accepted Predictions

The new feature allows users to view feedbacks and accepted predictions for a model by clicking on the "View Feedbacks" button in the training area. This will trigger the display of a modal window, similar to the one shown in the image below.

The modal window will display a list of feedbacks and accepted predictions for the model, providing users with a detailed understanding of the model's performance. This feature is particularly useful for model developers and evaluators who want to gain insights into the model's strengths and weaknesses, identify areas for improvement, and refine the model's performance.

Accessing Feedbacks and Accepted Predictions

Feedbacks and accepted predictions for a model can be accessed through the following APIs:

Feedbacks: https://api-prod.fair.hotosm.org/api/v1/feedback/?training=226
Approved predictions: https://api-prod.fair.hotosm.org/api/v1/approved-prediction/?training=226

These APIs provide a programmatic way to access feedbacks and accepted predictions for a model, enabling developers to integrate this feature into their applications and workflows.

Benefits of the Feature

The new feature offers several benefits, including:

Improved model evaluation: By providing a more comprehensive understanding of a model's performance, users can make more informed decisions about model development and deployment.
Enhanced model refinement: By identifying areas for improvement, users can refine the model's performance and achieve better results.
Increased transparency: By providing a detailed understanding of a model's performance, users can gain insights into the model's strengths and weaknesses, promoting transparency and accountability.

Implementation Details

The new feature is implemented using a combination of front-end and back-end technologies, including:

Front-end: The modal window is implemented using HTML, CSS, and JavaScript, providing a user-friendly interface for viewing feedbacks and accepted predictions.
Back-end: The API endpoints for accessing feedbacks and accepted predictions are implemented using a RESTful API, providing a programmatic way to access this data.

The new feature for viewing feedbacks and accepted predictions for a model is a significant enhancement to the evaluation experience, providing users with a more nuanced and informative understanding of a model's performance. By accessing feedbacks and accepted predictions through the provided APIs, users can gain insights into the model's strengths and weaknesses, identify areas for improvement, and refine the model's performance. We believe that this feature will be a valuable addition to the platform, promoting transparency, accountability, and improved model development and deployment.
Feature: Enhancing Model Evaluation with Feedbacks and Accepted Predictions

We've received many questions about the new feature for viewing feedbacks and accepted predictions for a model. Below, we've compiled a list of frequently asked questions and answers to help you better understand this feature.

Q: What is the purpose of the "View Feedbacks" button?

A: The "View Feedbacks" button is a new feature that allows users to view feedbacks and accepted predictions for a model. This button is located in the training area and can be clicked to display a modal window with a list of feedbacks and accepted predictions.

Q: What is the difference between feedbacks and accepted predictions?

A: Feedbacks are comments or ratings provided by users on the model's performance, while accepted predictions are predictions that have been reviewed and approved by the model's developers or evaluators.

Q: How do I access feedbacks and accepted predictions for a model?

A: You can access feedbacks and accepted predictions for a model by clicking on the "View Feedbacks" button in the training area. Alternatively, you can use the following APIs:

Feedbacks: https://api-prod.fair.hotosm.org/api/v1/feedback/?training=226
Approved predictions: https://api-prod.fair.hotosm.org/api/v1/approved-prediction/?training=226

Q: What information is displayed in the modal window?

A: The modal window displays a list of feedbacks and accepted predictions for the model, including the user's comment or rating, the date and time the feedback was provided, and the status of the prediction (e.g., approved, rejected, etc.).

Q: Can I filter or sort the feedbacks and accepted predictions in the modal window?

A: Yes, you can filter or sort the feedbacks and accepted predictions in the modal window using the filters and sorting options provided.

Q: How can I use the feedbacks and accepted predictions to improve the model's performance?

A: You can use the feedbacks and accepted predictions to identify areas for improvement in the model's performance. For example, if a user provides a negative comment or rating, you can review the model's performance and make adjustments to improve its accuracy or reliability.

Q: Can I integrate the feedbacks and accepted predictions into my own application or workflow?

A: Yes, you can integrate the feedbacks and accepted predictions into your own application or workflow using the provided APIs.

Q: Is the feedbacks and accepted predictions feature available for all models?

A: Yes, the feedbacks and accepted predictions feature is available for all models, but it may not be enabled for all models by default. You can contact our support team to request that the feature be enabled for a specific model.

Q: How do I provide feedback on a model's performance?

A: You can provide feedback on a model's performance by clicking on the "Provide Feedback" button in the training area. This will open a modal window where you can enter your comment or rating and submit it for review.

We hope this Q&A article has helped you better understand the new feature for viewing feedbacks and accepted predictions for a model. If you have any further questions or need assistance with using this feature, please don't hesitate to contact our support team.