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There are 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. Predicted Values Real Values 1) Find TN, TP, FP, FN. 2) Calculate accuracy, precision, recall, and fl-score. 3) Is the dataset balanced or imbalanced? 4) What performance metrics is the best if we don't have any information about what is the dataset about? 5) If 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? There are 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. Predicted Values Real Values 1) Find TN, TP, FP, FN. 2) Calculate accuracy, precision, recall, and fl-score. 3) Is the dataset balanced or imbalanced? 4) What performance metrics is the best if we don't have any information about what is the dataset about? 5) If 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