Fix formatting in README.md

README.md

@@ -37,17 +37,17 @@ The measure accounts for the hierarchical structure of the ISCO-08 classificatio
 2. The measure punishes distant errors more heavily:
     1. the measure gives higher evaluation for correctly classifying one level down compared to staying at the parent node, e.g. classification into node $E$ (ISCO minor group "111") is better than classification into its parent $C$ (ISCO sub-major group "11") since $E$ is closer to the correct category $G$; and
     2. the measure gives lower evaluation for incorrectly classifying one level down comparing to staying at the parent node, e.g. classification into node $F$ (ISCO unit group "1120") is worse than classification into its parent $C$ since $F$ is farther away from $G$.
-\n
-The features described are accomplished by pairing hierarchical variants of precision ($hP$) and recall ($hR$) to form a hierarchical F1 (hF_β) score where each sample belongs not only to its class (e.g., a unit group level code), but also to all ancestors of the class in a hierarchical graph (i.e., the minor, sub-major, and major group level codes).\n
-\n
-Hierarchical precision can be computed with:\n
-$hP = \frac{| \v{C}_i ∩ \v{C}^′_i|}  {|\v{C}^′_i |} = \frac{1}{2}$\n
-\n
-Hierarchical recall can be computed with:\n
-$hR = \frac{| \v{C}_i ∩ \v{C}^′_i|}  {|\v{C}_i |} = \frac{1}{2}$\n
-\n
-Combining the two values $hP$ and $hR$ into one hF-measure:\n
-hF_β = \frac{(β^2 + 1) · hP · hR}{(β^2 · hP + hR)}, β ∈ [0, +∞)
+
+The features described are accomplished by pairing hierarchical variants of precision ($hP$) and recall ($hR$) to form a hierarchical F1 (hF_β) score where each sample belongs not only to its class (e.g., a unit group level code), but also to all ancestors of the class in a hierarchical graph (i.e., the minor, sub-major, and major group level codes).
+
+Hierarchical precision can be computed with:
+$hP = \frac{| \v{C}_i ∩ \v{C}^′_i|}  {|\v{C}^′_i |} = \frac{1}{2}$
+
+Hierarchical recall can be computed with:
+$hR = \frac{| \v{C}_i ∩ \v{C}^′_i|}  {|\v{C}_i |} = \frac{1}{2}$
+
+Combining the two values $hP$ and $hR$ into one hF-measure:
+$hF_β = \frac{(β^2 + 1) · hP · hR}{(β^2 · hP + hR)}, β ∈ [0, +∞)$
 
 ## How to Use
 *Give general statement of how to use the metric*