Update 21_logistic_regression.py

probability/21_logistic_regression.py

@@ -24,7 +24,7 @@ def _(mo):
 
         _This notebook is a computational companion to ["Probability for Computer Scientists"](https://chrispiech.github.io/probabilityForComputerScientists/en/part5/log_regression/), by Stanford professor Chris Piech._
 
-        Logistic Regression is a classification algorithm (I know, terrible name. Perhaps Logistic Classification would have been better) that works by trying to learn a function that approximates $P(y|x)$. It makes the central assumption that $P(y|x)$ can be approximated as a sigmoid function applied to a linear combination of input features. It is particularly important to learn because logistic regression is the basic building block of artificial neural networks.
+        Logistic Regression is a classification algorithm that learns a function approximating $P(y|x)$, and can be used to make a classifier. It makes the central assumption that $P(y|x)$ can be approximated as a sigmoid function applied to a linear combination of input features. It is particularly important to learn because logistic regression is the basic building block of artificial neural networks.
         """
     )
     return