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| {{see also|Machine learning terms}}
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| ===Introduction==
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| Machine learning demands the evaluation of a model's performance. One way to do this is through classification metrics like true positive rate (TPR). TPR measures how well a model can correctly identify positive instances. In this article, we'll define TPR and discuss its significance in machine learning.
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| ==Definition==
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| True Positive Rate (TPR) is a classification metric that measures the proportion of actual positive instances a model correctly recognizes as such. Put another way, TPR measures true positives relative to all true positives plus any false negatives.
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| ==Formula==
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| Calculating TPR ($$TPR) involves multiplying by 100 the true positives and false negatives minus $$.
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| ==Importance==
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| TPR (Token Pricing Ratio) is an important metric in many machine learning applications, such as medical diagnosis, fraud detection and spam filtering. Accurately recognizing positive instances is paramount; for instance in medical diagnosis it could mean the difference between life and death. Therefore, models with higher TPRs tend to be preferred over those with low ones.
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| ==Interpretation==
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| A high TPR indicates the model is correctly recognizing most positive instances, while a low TPR indicates it fails to identify an excessive number of such instances. TPR often works in combination with other classification metrics like precision, recall, and F1 score for a more complete assessment of a model's performance.
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| ==Factors affecting TPR==
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| Factors such as data quality and quantity, algorithm selection, and threshold value all impact TPR. For instance, if two classes have significantly fewer instances than each other in the training set, a model could achieve high TPR by simply classifying everything as the majority class. In such cases, additional metrics like precision or recall (F1 score) provide more precise assessments of the model's performance.
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| ==Explain Like I'm 5 (ELI5)==
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| Machine learning seeks to create a computer program that can accurately determine whether something is good or bad, such as whether an email is spam. True Positive Rate (TPR) measures how well our program can identify positives from negatives - it's like asking someone to pick all of the red apples from a basket of apples and measuring their accuracy; if they got most of them right, their TPR will be high; on the other hand, if they missed many red apples they have a low TPR.
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| ==Explain Like I'm 5 (ELI5)==
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| Imagine you have a basket of apples and you need to pick out all the red apples. Your task is to ensure all are picked, but occasionally an accidental green apple may slip by.
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| The true positive rate (TPR) is a measure of your skill at picking out red apples. It's the number of red apples picked divided by the total number of red apples in the basket. The higher this number is, the better at you are at distinguishing between red and green apples by mistake.
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| In machine learning, we use true positive rate to gauge how well a model does at correctly recognizing things we want it to find. For instance, if we want the model to identify all pictures of cats in an image set, its true positive rate tells us how well it does at accurately distinguishing those photos as positive examples. The higher this number is, the better able the model is at finding cat pictures and not mistakenly misidentifying other images as cat ones.
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| [[Category:Terms]] [[Category:Machine learning terms]]
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