Closed gavinjalberghini closed 2 years ago
Description: It is important to understand how we can measure the effectiveness of ML algorithms. Read the following articles and be ready to discuss them at the next meeting.
ML Metrics - https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithm-f10ba6e38234 Classification and Regression Metrics - https://towardsdatascience.com/20-popular-machine-learning-metrics-part-1-classification-regression-evaluation-metrics-1ca3e282a2ce
Acceptance Criteria: Using the confusion matrix in #files on Discord, calculate the specified metrics.
Classification Accuracy = ? Precision = ? Recall = ? F1-Score = ? Sensitivity = ? Specificity = ?
V: 1
Description: It is important to understand how we can measure the effectiveness of ML algorithms. Read the following articles and be ready to discuss them at the next meeting.
ML Metrics - https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithm-f10ba6e38234 Classification and Regression Metrics - https://towardsdatascience.com/20-popular-machine-learning-metrics-part-1-classification-regression-evaluation-metrics-1ca3e282a2ce
Acceptance Criteria: Using the confusion matrix in #files on Discord, calculate the specified metrics.
Classification Accuracy = ? Precision = ? Recall = ? F1-Score = ? Sensitivity = ? Specificity = ?
V: 1