Post by account_disabled on Dec 13, 2023 3:36:14 GMT -6
Resources for contacting and sending campaigns are wasted. Type 2 Error (False Negative – FN) : Type 2 Error occurs when the ML Model makes a prediction that the customer or target audience for marketing is not the target group we want (Negative), but in reality, this group of customers is the target group we are interested in. For example, when the model says that this group of customers is not the target group. But in fact, they are more likely to respond to the campaign. or purchase additional products This is what results in us not doing proper marketing. or offer a campaign that is good enough for this group .
Loss of opportunity to increase sales and Whatsapp Number List marketing. Marketers and AI: Confusion Matrix and principles for evaluating ML model performance source: Evaluating ML Model Performance with Confusion Matrix Now comes the important part of the article. That is, it is a matter of using the values in the Confusion Matrix table to evaluate the performance of the ML Model through calculating four evaluation parameters: Accuracy (Accuracy), Precision in predicting the target group (Precision), and resources for contacting and sending campaigns are wasted. Type 2 Error (False Negative – FN) : Type 2 Error occurs when the ML Model makes a prediction that the customer or target audience for marketing is not the target group we want (Negative), but in reality, this group of customers is the target group we are interested in. For example, when the model says that this group of customers is not the target group.
But in fact, they are more likely to respond to the campaign. or purchase additional products This is what results in us not doing proper marketing. or offer a campaign that is good enough for this group Loss of opportunity to increase sales and marketing. Marketers and AI: Confusion Matrix and principles for evaluating ML model performance source: Evaluating ML Model Performance with Confusion Matrix Now comes the important part of the article. That is, it is a matter of using the values in the Confusion Matrix table to evaluate the performance of the ML Model through calculating four evaluation parameters: Accuracy (Accuracy), Precision in predicting the target group (Precision).
Loss of opportunity to increase sales and Whatsapp Number List marketing. Marketers and AI: Confusion Matrix and principles for evaluating ML model performance source: Evaluating ML Model Performance with Confusion Matrix Now comes the important part of the article. That is, it is a matter of using the values in the Confusion Matrix table to evaluate the performance of the ML Model through calculating four evaluation parameters: Accuracy (Accuracy), Precision in predicting the target group (Precision), and resources for contacting and sending campaigns are wasted. Type 2 Error (False Negative – FN) : Type 2 Error occurs when the ML Model makes a prediction that the customer or target audience for marketing is not the target group we want (Negative), but in reality, this group of customers is the target group we are interested in. For example, when the model says that this group of customers is not the target group.
But in fact, they are more likely to respond to the campaign. or purchase additional products This is what results in us not doing proper marketing. or offer a campaign that is good enough for this group Loss of opportunity to increase sales and marketing. Marketers and AI: Confusion Matrix and principles for evaluating ML model performance source: Evaluating ML Model Performance with Confusion Matrix Now comes the important part of the article. That is, it is a matter of using the values in the Confusion Matrix table to evaluate the performance of the ML Model through calculating four evaluation parameters: Accuracy (Accuracy), Precision in predicting the target group (Precision).