small edit to check formula

README.md

@@ -22,8 +22,9 @@ pinned: false
 --> 
 Expected Calibration Error `ECE` is a standard metric to evaluate top-1 prediction miscalibration. 
 It measures the L^p norm difference between a model’s posterior and the true likelihood of being correct.
-$$ ECE_p(f)^p= \mathbb{E}_{(X,Y)} \left[\|\mathbb{E}[Y = \hat{y} \mid f(X) = \hat{p}] - f(X)\|^p_p\right]$$, where $\hat{y} = \argmax_{y'}[f(X)]_y'$ is a class prediction with associated posterior probability $\hat{p}= \max_{y'}[f(X)]_y'$.
-
+```
+$$ ECE_p(f)^p= \mathbb{E}_{(X,Y)} \left[\|\mathbb{E}[Y = \hat{y} \mid f(X) = \hat{p}] - f(X)\|^p_p\right]$$, where $$ \hat{y} = \argmax_{y'}[f(X)]_y'$$ is a class prediction with associated posterior probability $$ \hat{p}= \max_{y'}[f(X)]_y'$$.
+```
 It is generally implemented as a binned estimator that discretizes predicted probabilities into a range of possible values (bins) for which conditional expectation can be estimated.
 
 As a metric of calibration *error*, it holds that the lower, the better calibrated a model is.