<h1 id="heading-true-positive-rate-andamp-true-negative-rate">True Positive Rate &amp; True Negative Rate</h1>
<h3 id="heading-true-positive-rate">True Positive Rate:</h3>
<ul>
<li><strong>Sensitivity (SN) is calculated as the number of correct positive predictions divided by the total number of positives. It is also called Recall (REC) or true positive rate (TPR). The best sensitivity is 1.0, whereas the worst is 0.0.</strong></li>
</ul>
<blockquote>
<p>$$SN = TP / TP*FN = TP / P$$</p>
</blockquote>
<h3 id="heading-true-negative-rate">True Negative Rate:</h3>
<ul>
<li><strong>Specificity (SP) is calculated as the number of correct negative predictions divided by the total number of negatives. It is also called a true negative rate (TNR). The best specificity is 1.0, whereas the worst is 0.0.</strong></li>
</ul>
<blockquote>
<p>$$SN = TP / TP*FN = TP / P$$</p>
</blockquote>


# True Positive Rate & True Negative Rate

### True Positive Rate:

* **Sensitivity (SN) is calculated as the number of correct positive predictions divided by the total number of positives. It is also called Recall (REC) or true positive rate (TPR). The best sensitivity is 1.0, whereas the worst is 0.0.**
    

> $$SN = TP / TP*FN = TP / P$$

### True Negative Rate:

* **Specificity (SP) is calculated as the number of correct negative predictions divided by the total number of negatives. It is also called a true negative rate (TNR). The best specificity is 1.0, whereas the worst is 0.0.**
    

> $$SN = TP / TP*FN = TP / P$$

True Positive Rate & True Negative Rate

#21 Machine Learning & Data Science Challenge 21

True Positive Rate &amp; True Negative Rate

True Positive Rate:

True Negative Rate:

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#21 Machine Learning & Data Science Challenge 21

Table of contents

True Positive Rate & True Negative Rate

True Positive Rate:

True Negative Rate: