Tweak formatting of tuning page in docs

docs/tuning.md

@@ -28,8 +28,8 @@ Some things I try out to see if they help:
 2. Try setting `adaptive_parsimony_scaling` a bit larger, maybe up to `1000`.
 3. Sometimes I try using `warmup_maxsize_by`. This is useful if you find that the search finds a very complex equation very quickly, and then gets stuck. It basically forces it to start at the simpler equations and build up complexity slowly.
 4. Play around with different losses:
-    i. I typically try `L2DistLoss()` and `L1DistLoss()`. L1 loss is more robust to outliers compared to L2, so is often a good choice for a noisy dataset. 
-    ii. I might also provide the `weights` parameter to `fit` if there is some reasonable choice of weighting. For example, maybe I know the signal-to-noise of a particular row of `y` - I would set that SNR equal to the weights. Or, perhaps I do some sort of importance sampling, and weight the rows by importance.
+    - I typically try `L2DistLoss()` and `L1DistLoss()`. L1 loss is more robust to outliers compared to L2 (L1 finds the median, while L2 finds the mean of a random variable), so is often a good choice for a noisy dataset. 
+    - I might also provide the `weights` parameter to `fit` if there is some reasonable choice of weighting. For example, maybe I know the signal-to-noise of a particular row of `y` - I would set that SNR equal to the weights. Or, perhaps I do some sort of importance sampling, and weight the rows by importance.
 
 Very rarely I might also try tuning the mutation weights, the crossover probability, or the optimization parameters. I never use `denoise` or `select_k_features` as I find they aren't very useful.