Shall you fire her or promote her?

Learning Goal: I’m working on a data analytics test / quiz prep and need an explanation and answer to help me learn.2.Below you see two columns coming from a classification problem. The first column is the predicted value, the second is the real value. A value of 1 stands for positive/yes, and 0 means negative or no. Find TN, TP, FP, FN.Predicted Values Real Values0 01 10 00 00 01 01 01 10 00 00 11 10 01 10 00 10 01 11 10 01 10 01 11 01 10 00 01 11 11 10 00 01 11 10 00 00 01 10 01 10 01 10 00 10 01 10 00 00 01 11 11 11 10 00 01 11 11 11 10 01 11 10 00 11 10 01 10 00 01 10 00 00 00 01 10 01 11 01 10 01 10 00 01 00 01 11 01 10 01 10 11 11 01 10 01 1Find TP, TN, FP, and FN:(15 Points)TP = 41, TN = 43, FP = 7, FN = 5TP = 43, TN = 41, FP = 7, FN = 5TP = 41, TN = 43, FP = 5, FN = 7TP = 43, TN = 41, FP = 5, FN = 73.For question 2, calculate accuracy, precision, recall, and f1-score.(10 Points)acc = 0.87, pre = 0.85, rec = 0.89, f1 = 0.87acc = 0.87, pre = 0.89, rec = 0.85, f1 = 0.87acc = 0.87, pre = 0.85, rec = 0.89, f1 = 0.85acc = 0.85, pre = 0.89, rec = 0.87, f1 = 0.854.For question 2, is the dataset balanced or imbalanced?(10 Points)BalancedImbalancedNot enough information is given5.For question 2, which performance metrics will you choose if we don’t have any information about what is the dataset about?(10 Points)AccuracyPrecisionRecallF1-scoreWe cannot choose a metric when we don’t know what the data is about6.For question 2, let’s assume 0 stands for not-rainy and 1 stands for rainy days. A business of a large chain ice cream store is using this model to close some branches if it is predicted to rain. A night before, based on model prediction, employees receive an email if they need to show up tomorrow at work or not.The company is already well-known and doesn’t need to acquire more customers. Their focus for now is to save costs and not open the store, when there is no customer due to the rain. With this background, which performance metrics will you choose?(15 Points)AccuracyPrecisionRecallF1-score7.You are a manager in customer service department in Amazon. Daily, your team takes care of 100,000 customer emails. You need to develop a model, that scans those emails into two general categories: complaints and approvals. Complaints must be sent to a specific department to take care of them, either by refunding the customer or giving them some promotions …Approvals need to be sent to another department, to try to encourage happy customers more to purchase more using a variety of techniques. Approval is labelled as positive and complaints are labelled as negative. Surely, you understand the importance of reading those emails by a machine and classifying them into correct category. Your model is not perfect, though:For every TN, in average, we make $10, for every TP we make, $8, and for every FP we will lose $3 and for every FN we will lose $12. For your model, TN=20, TP=90, FN=15, FP=2. All numbers are in millionsWhich performance metrics will you use for this model?(10 Points)AccuracyPrecisionRecallF1-Score8.For question 7, what is the expected value of the model?(15 Points) For question 7, per each email, you are paying $0.1 to your data scientist to do the prediction. Based on EV you have, shall you fire her or promote her?(15 Points)Fire herKeep her, and promote herNot enough data to decide
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