Evaluation indicators of machine learning algorithms are metrics used to assess the performance and accuracy of models. Common indicators include accuracy, precision, recall, F1 score, and area under the ROC curve (AUC-ROC).
Accuracy: Measures the proportion of correct predictions out of all predictions made. It's calculated as (TP + TN) / (TP + TN + FP + FN), where TP is true positives, TN is true negatives, FP is false positives, and FN is false negatives.
Example: If a model correctly predicts 90 out of 100 samples, its accuracy is 90%.
Precision: Indicates the accuracy of positive predictions. It's calculated as TP / (TP + FP). A high precision means that the model has a low false positive rate.
Example: If a model predicts 80 positive cases and among them, 70 are correct, precision is 70 / 80 = 87.5%.
Recall (Sensitivity): Measures the ability of the model to find all positive samples. It's calculated as TP / (TP + FN). High recall indicates a low false negative rate.
Example: If there are 90 actual positive cases and the model identifies 80, recall is 80 / 90 ≈ 88.9%.
F1 Score: Balances precision and recall, providing a single metric that considers both false positives and false negatives. It's the harmonic mean of precision and recall, calculated as 2 * (Precision * Recall) / (Precision + Recall).
Example: If precision is 87.5% and recall is 88.9%, the F1 score is approximately 88.2%.
Area Under the ROC Curve (AUC-ROC): Represents the degree of separability. The higher the AUC, the better the model is at distinguishing between classes.
Example: An AUC-ROC of 0.95 indicates excellent separability.
In the context of cloud computing, platforms like Tencent Cloud offer services that facilitate the deployment and evaluation of machine learning models. For instance, Tencent Cloud's AI Platform provides tools for model training, evaluation, and optimization, leveraging these indicators to help users assess and improve their models' performance.