conmatrix : Confusion Matrix # Data Imbalance # Evaluation # Weights & Biases
- 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