Balanced Accuracy \`Score\` in Tensorflow
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Introduction
In machine learning, classification problems are often evaluated using various performance metrics. One such metric is the Balanced Accuracy Score, particularly useful when dealing with imbalanced datasets. This article will dive deep into understanding the Balanced Accuracy Score, its significance, and how it can be implemented in TensorFlow.
What is Balanced Accuracy Score?
Balanced Accuracy `Score` is a metric used to evaluate the performance of a classification model, especially in the context of imbalanced classes. It accounts for the model's ability to correctly classify both the majority and minority classes. The primary formula for balanced accuracy is:
Here: • `TP` = True Positives • `TN` = True Negatives • `FP` = False Positives • `FN` = False Negatives
Balanced Accuracy `Score` gives a fair evaluation even when the dataset is skewed, as it takes into account the true positive rate and true negative rate equally.
Balanced Accuracy vs. Other Metrics
| Metric | Description | Suitability |
| Balanced Accuracy | Average of sensitivity (recall) and specificity. | Suitable for imbalanced datasets as it provides a fair assessment across classes. |
| Accuracy | Ratio of correctly predicted instances to total instances. | Can be misleading on imbalanced datasets, e.g., predicting majority class only still gives high accuracy. |
F1 Score | Harmonic mean of precision and recall. | Useful for imbalanced classes but does not account for true negatives. |
| AUC-ROC | Area under the Receiver Operating Characteristic curve. | Effective for binary classifications and considers different thresholds, but doesn't directly handle class imbalance. |
Implementing Balanced Accuracy in TensorFlow
TensorFlow, a powerful open-source library for machine learning, offers a flexible platform to implement and evaluate different models. Although TensorFlow does not directly provide a built-in function for balanced accuracy, it can be computed using TensorFlow's metrics.
TensorFlow Example
