Some selected typos

doc/src/Regression/Regression.do.txt

@@ -2830,7 +2830,7 @@ classes).
 
 The goal is to predict the
 output classes from the design matrix $X\in\mathbb{R}^{n\times p}$
-made of $n$ samples, each of which bears $p$ features. Of cours e the
+made of $n$ samples, each of which bears $p$ features. The
 primary goal is to identify the classes to which new unseen samples
 belong.
  
@@ -2850,7 +2850,18 @@ $\mathbf{x}_i = (1,\boldsymbol{x}_i)$ and $\mathbf{w}_i = (b_0,\boldsymbol{w}_i)
 !split
 ===== Some selected properties =====
 
-This function takes values on the entire real axis. In the case of logistic regression, however, the labels $y_i$ are discrete variables. One simple way to get a discrete output is to have sign functions that map the output of a linear regressor to $\{0,1\}$,  $f(s_i)=$ sign$(s_i) = 1$ if $s_i\ge 0$ and 0 if otherwise. Indeed, this is commonly known as the ``perceptron" in the machine learning literature. This model is extremely simple, and it is favorable in many cases (e.g. noisy data) to have a ``soft" classifier that outputs the probability of a given category. For example, given $\mathbf{x}_i$, the classifier outputs the probability of being in category $m$. One such function is the logistic (or sigmoid) function:
+This function takes values on the entire real axis. In the case of
+logistic regression, however, the labels $y_i$ are discrete
+variables. One simple way to get a discrete output is to have sign
+functions that map the output of a linear regressor to $\{0,1\}$,
+$f(s_i)=sign(s_i)=1$ if $s_i\ge 0$ and 0 if otherwise. Indeed,
+this is commonly known as the ``perceptron" in the machine learning
+literature. This model is extremely simple, and it is favorable in
+many cases (e.g. noisy data) to have a ``soft" classifier that outputs
+the probability of a given category. For example, given
+$\mathbf{x}_i$, the classifier outputs the probability of being in
+category $m$. One such function is the logistic (or sigmoid) function:
+
 !bt
 \begin{equation}
 f(s) = \frac{1}{1+\mathrm e^{-s}}.