https://github.com/miozilla/conmatrix
conmatrix :1234::alien::checkered_flag: : Confusion Matrix # Data Imbalance # Evaluation # Weights & Biases
https://github.com/miozilla/conmatrix
accuracy azureml bias binary-classification classification-model confusion-matrix dataset evaluate false-positive-rate fn fp imbalanced-data precision recall roc sensitivity specificity tn tp weight
Last synced: 3 months ago
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conmatrix :1234::alien::checkered_flag: : Confusion Matrix # Data Imbalance # Evaluation # Weights & Biases
- Host: GitHub
- URL: https://github.com/miozilla/conmatrix
- Owner: miozilla
- Created: 2025-09-20T17:00:57.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-09-20T17:59:26.000Z (4 months ago)
- Last Synced: 2025-09-20T19:09:31.034Z (4 months ago)
- Topics: accuracy, azureml, bias, binary-classification, classification-model, confusion-matrix, dataset, evaluate, false-positive-rate, fn, fp, imbalanced-data, precision, recall, roc, sensitivity, specificity, tn, tp, weight
- Language: Jupyter Notebook
- Homepage:
- Size: 2.94 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# conmatrix 🔢👽🏁
conmatrix : Confusion Matrix # Data Imbalance # Evaluation # Weights & Biases
## Objective
- Build a confusion matrix
- Assess performance of classification models.
- Resolve biases in a classification model
- Evaluate results of binary classification models using a confusion matrix.
- Use weighted classes to address class imbalances when training a model and evaluating the results.
- Review metrics to improve classification models.
- Mitigate performance issues from data imbalances.
- Calculate the very basic measurements used in the evaluation of classification models: TP, FP, TN, FN.
- Use the measurement aboves to calculate more meaningful metrics, such as:
- Accuracy
- Sensitivity/Recall
- Specificity
- Precision
- False positive rate
## Confusion Matrix & Data Imbalances






















