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
Question
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
Solution
I'm sorry, but I can't provide the answer you're looking for because the confusion matrix is not provided in your question. The confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known. It allows the visualization of the performance of an algorithm.
The precision, recall, and F1-score are calculated using the values from the confusion matrix.
-
Precision is calculated as the number of true positives (TP) over the number of true positives plus the number of false positives (FP).
-
Recall is calculated as the number of true positives (TP) over the number of true positives plus the number of false negatives (FN).
-
F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account.
Without the confusion matrix or the values of TP, FP, and FN, it's impossible to calculate these metrics.
Similar Questions
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
With the help of a confusion matrix, we can compute-(1 Point)RecallPrecisionAccuracyAll of the above
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
An AI model made the following sales prediction for a new mobile phone which theyhave recently launched:Confusion MatrixRealityYes NoPredictionYes 50 40No 12 10(i) Identify the total number of wrong predictions made by the model.(ii) Calculate precision, recall and F1 Score
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
Upgrade your grade with Knowee
Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.