Consider a classification problem with three classes: A, B, and C. A machine learning model is trained on a labeled dataset, and the confusion matrix for the model's predictions is given below:What is the overall accuracy of the model?a)0.69b)0.85c)0.8d)0.725

Given the confusion matrix for a disease prediction model, calculate the Precision, Recall, and F1-Score, and choose the correct values from the options below.1 pointPrecision: 0.800, Recall: 0.750, F1-Score: 0.774Precision: 0.778, Recall: 0.700, F1-Score: 0.737Precision: 0.850, Recall: 0.600, F1-Score: 0.706Precision: 0.900, Recall: 0.650, F1-Score: 0.756

48.Consider a model that predicts 150 examples for the positive class, 95 are correct (true positives), meaning five were missed (false negatives) and 55 are incorrect (false positives). What will be the precision?  A. 55/150  B. 95/150  C. 55/95  D. 150/55

For the given confusion matrix, compute the recall True Positive True NegativePredicted Positive 8 3Predicted Negative 2 7 0.73 0.7 0.78 0.8

Explain the Confusion Matrix with Respect to Machine Learning Algorithms

What is the purpose of a confusion matrix in machine learning?To visualize complex datasetsTo describe the distribution of the datasetTo evaluate the performance of a classification modelTo reduce overfitting in models