Post by account_disabled on Dec 13, 2023 9:33:41 GMT
False Positives (FP) : is the amount of data that is incorrectly predicted in the category of interest. In this case, the data was predicted to be positive, but the actual data turned out to be negative, that is, the wrong prediction was what we had assessed, or False. False Negatives (FN) : is the amount of data that is misclassified into categories that we are not interested in. In this case, the data was predicted to be negative, but the actual data was positive. That is, the wrong prediction was not what we had assessed. How are you? After reading this, you'll definitely be confused by the name. Therefore, Nick gives a simple trick: The word True or False in front indicates whether or not what our ML Model has estimated is correct. Wrong when compared to real data.
As for the words Positive or Negative, they refer to Whatsapp Number List the results obtained from the ML Model. Marketers and AI: Confusion Matrix and principles for evaluating ML model performance source: -dive-into-confusion-matrixt Error types: Type1 (FP) and Type2 (FN) What we as marketers need to understand more about. False Positives (FP) : is the amount of data that is incorrectly predicted in the category of interest. In this case, the data was predicted to be positive, but the actual data turned out to be negative, that is, the wrong prediction was what we had assessed, or False. False Negatives (FN) : is the amount of data that is misclassified into categories that we are not interested in. In this case, the data was predicted to be negative, but the actual data was positive.
That is, the wrong prediction was not what we had assessed. How are you? After reading this, you'll definitely be confused by the name. Therefore, Nick gives a simple trick: The word True or False in front indicates whether or not what our ML Model has estimated is correct. Wrong when compared to real data. As for the words Positive or Negative, they refer to the results obtained from the ML Model. Marketers and AI: Confusion Matrix and principles for evaluating ML model performance source: -dive-into-confusion-matrixt Error types: Type1 (FP) and Type2 (FN) What we as marketers need to understand more about.
As for the words Positive or Negative, they refer to Whatsapp Number List the results obtained from the ML Model. Marketers and AI: Confusion Matrix and principles for evaluating ML model performance source: -dive-into-confusion-matrixt Error types: Type1 (FP) and Type2 (FN) What we as marketers need to understand more about. False Positives (FP) : is the amount of data that is incorrectly predicted in the category of interest. In this case, the data was predicted to be positive, but the actual data turned out to be negative, that is, the wrong prediction was what we had assessed, or False. False Negatives (FN) : is the amount of data that is misclassified into categories that we are not interested in. In this case, the data was predicted to be negative, but the actual data was positive.
That is, the wrong prediction was not what we had assessed. How are you? After reading this, you'll definitely be confused by the name. Therefore, Nick gives a simple trick: The word True or False in front indicates whether or not what our ML Model has estimated is correct. Wrong when compared to real data. As for the words Positive or Negative, they refer to the results obtained from the ML Model. Marketers and AI: Confusion Matrix and principles for evaluating ML model performance source: -dive-into-confusion-matrixt Error types: Type1 (FP) and Type2 (FN) What we as marketers need to understand more about.