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True negative (TN): Difference between revisions
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{{see also|Machine learning terms}} | |||
==Introduction== | ==Introduction== | ||
[[True negative (TN)]] is when the [[machine learning model]] correctly predicts the [[negative class]]. [[Machine learning]] [[classification]] is the process of accurately predicting a data point's class based on features. This classification can lead to four distinct outcomes: [[true positive (TP)]], [[true negative (TN)]], [[false positive (FP)]] and [[false negative (FN)]]. | [[True negative (TN)]] is when the [[machine learning model]] correctly predicts the [[negative class]]. [[Machine learning]] [[classification]] is the process of accurately predicting a data point's class based on features. This classification can lead to four distinct outcomes: [[true positive (TP)]], [[true negative (TN)]], [[false positive (FP)]] and [[false negative (FN)]]. | ||
==What is True Negative (TN)?== | ==What is True Negative (TN)?== | ||
True Negative (TN) is one of the possible outcomes in a binary classification problem when the model predicts a negative result when | True Negative (TN) is one of the possible outcomes in a [[binary classification]] problem when the [[model]] predicts a negative result when the result or [[label]] is in fact negative. In other words, when the model correctly recognizes a data point as not belonging to any class, it is treated as a true negative. | ||
Let us consider a machine learning model that predicts whether an email is spam or not. If the model correctly predicts that an email is not spam and it indeed is not, this would be considered a true negative result, meaning the model correctly identified that this input data point does not belong in a certain class. | Let us consider a machine learning model that predicts whether an email is spam or not. If the model correctly predicts that an email is not spam and it indeed is not, this would be considered a true negative result, meaning the model correctly identified that this input data point does not belong in a certain class. | ||
==Why is True Negative important?== | ==Why is True Negative important?== | ||
True negatives are essential in machine learning as they help gauge the overall accuracy of a model. Accuracy refers to how accurately it predicts correct outcomes; an abundance of true negatives indicates that the model is performing well and can be trusted to make accurate predictions. | True negatives are essential in machine learning as they help gauge the overall [[accuracy]] of a model. Accuracy refers to how accurately it predicts correct outcomes; an abundance of true negatives indicates that the model is performing well and can be trusted to make accurate predictions. | ||
In some instances, the true negative rate is more critical than the true positive rate. For instance, in medical diagnosis, a high true negative rate helps prevent false | In some instances, the true negative rate is more critical than the true positive rate. For instance, in medical diagnosis, a high true negative rate helps prevent [[false positive]]s which could lead to unnecessary medical treatments or surgeries. | ||
==Factors that affect True Negative== | ==Factors that affect True Negative== | ||
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==Explain Like I'm 5 (ELI5)== | ==Explain Like I'm 5 (ELI5)== | ||
Machine learning relies on computer programs to determine whether something is good or bad. If the program says something is bad and it turns out to be true, we call that a "true negative." This indicates the program has done an effective job at warning us when something is detrimental. As such, accuracy in its judgments must be ensured in order for us to trust it with accurate predictions about whether something should be praised or avoided. | Machine learning relies on computer programs to determine whether something is good or bad. If the program says something is bad and it turns out to be true, we call that a "true negative." This indicates the program has done an effective job at warning us when something is detrimental. As such, accuracy in its judgments must be ensured in order for us to trust it with accurate predictions about whether something should be praised or avoided. | ||
[[Category:Terms]] [[Category:Machine learning terms]] | |||