Learn what confusion matrices and classification reports are, how they differ, and when to use them for machine learning evaluation and validation.

Having 100 percent true positive rate gives a 1x1 matrix. This can cause bugs if you are using the confusion matrix in a function to calculate evaluation parameters such as sensitivity, auc, specifity ...

Evaluation Metrics: Assesses model performance using accuracy, F1 score, precision, recall, and a confusion matrix. Data The dataset consists of NASDAQ daily stock prices, including: Open and Close ...