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[Outreachy applications] Learning from misclassifications #63

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dzeber opened this issue Mar 13, 2020 · 0 comments · Fixed by #74, #98, #112 or #160
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[Outreachy applications] Learning from misclassifications #63

dzeber opened this issue Mar 13, 2020 · 0 comments · Fixed by #74, #98, #112 or #160

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@dzeber
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dzeber commented Mar 13, 2020 •
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When training a classification model, it is common to look at accuracy and the confusion matrix, which give a summary view of misclassifications. By itself, these metrics are informative but not very actionable.

Develop a metric or visualization that reveals something more about each misclassified point (beyond just the fact that it was misclassified) that can be used to improve the model.

Some examples of a metric might be the classification probability scores for the different classes, which can indicate whether the misclassified points were close to the decision boundary or not, or the distance from the class mean in feature space, indicating whether the misclassified points are outliers.

A good place to start is to study the misclassifications you got from your model for task #2. What do they tell you about how to improve your model?
KaairaGupta added a commit to KaairaGupta/PRESC that referenced this issue Mar 14, 2020
@KaairaGupta
completed first attemp at mozilla#63
5d34aff
@Soniyanayak51 Soniyanayak51 mentioned this issue Mar 14, 2020
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@iamarchisha iamarchisha mentioned this issue Mar 17, 2020
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mlopatka pushed a commit that referenced this issue Mar 20, 2020
@KaairaGupta
For #63: Learning from misclassification (#64)
e75320b 
* WIP: created function to plot classification probablities for misclassified data points

* completed first attemp at #63

* minor updates in histogram plots in function plot_misclassified_probablities

* plot correct classes
mlopatka pushed a commit that referenced this issue Mar 20, 2020
@iamarchisha
[ Fixes: #63 ]Learn from Misclassification (#74)
8b8bdcc 
* adds a module to visualize misclassification and tests it on winequality.csv

* adds .ipynb and .py for learning from misclassififcations

* Delete visualize_misclass.py

* Delete winequality.ipynb

* Delete winequality_modules.py
@mlopatka mlopatka reopened this Mar 20, 2020
@Sidrah-Madiha Sidrah-Madiha mentioned this issue Mar 21, 2020
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@Addi-11 Addi-11 mentioned this issue Mar 22, 2020
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@dzeber dzeber reopened this Mar 26, 2020
@mlopatka mlopatka reopened this Mar 27, 2020
@namrathagopalabhatla namrathagopalabhatla mentioned this issue Apr 5, 2020
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@dzeber dzeber changed the title Learning from misclassifications [Outreachy applications] Learning from misclassifications Jul 14, 2020
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@dzeber @mlopatka