-
Notifications
You must be signed in to change notification settings - Fork 51
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
[Outreachy applications] Visualization for misclassifications #7
Comments
This issue seems interesting. May I be assigned this? |
You're welcome to work on it and submit a contribution! We're not assigning issues currently to allow people to bring multiple perspectives or approaches to these questions. |
how can we contribute in the project should we PR then you assign us task ? |
@shristi428 You can submit a PR referencing the issue you worked on. |
Hey @dzeber! What dataset do we use to test out our code? |
@namrathagopalabhatla whichever you like. It might be a good start to use the same data & model you used for #2. |
…of classes (#86) * Contribution to issue #2 * Eliminació de l'arxiu de prova * Eliminació de l'arxiu de prova * Fixed axes when not numbers and removed superfluous function. * Initial commit to Issue #7 Visualization of missclassifications. * Adding explanatory graphs for the notebook regarding Issue #7 * Better graph to illustrate option 'hits-fails'.
* #7 Visualization for misclassification * Comparing test sample classifications between models I compared the random forest and k nearest neighbors classifier models and used a barchart to visualize the classification of the test set * added probability to misclasification visualization * new misclassification visualization method used * moved into misclassification_visualization folder * moved to misclassification visualization folder * Traversal of the space of train-test splits * fixed file path and did better visualization * Update #7 visualization for misclassifications.ipynb * Update misclassification_function.py * made changes to #7 * Delete Traversal of the space of train-test splits #3.ipynb * Delete traversal_function.py * Traversal of the space of train-test splits #3
Misclassifications can reveal a lot about the boundaries of performance of a classifier. Develop a visualization that helps dig into misclassified datapoints in the test set. A simple approach for a binary classifier would be to plot a histogram of the predicted class probabilities across the misclassified test samples in each class.
The text was updated successfully, but these errors were encountered: