clean up latex
@@ -183,9 +183,9 @@ Penalties and sparse regression
It's possible to add a penalizer term to the Cox regression as well. One can use these to i) stabilize the coefficients, ii) shrink the estimates to 0, iii) encourages a Bayesian interpretation, and iv) create sparse coefficients. Regression models, including the Cox model, include both an L1 and L2 penalty:
-.. math:: \frac{1}{2} \text{penalizer} ((1-l1\_ratio)\cdot ||\beta||_2^2 + l1\_ratio\cdot ||beta||_1)
+.. math:: \frac{1}{2} \text{penalizer} \left((1-\text{l1\_ratio}) \cdot ||\beta||_2^2 + \text{l1\_ratio} \cdot ||\beta||_1\right)
-Both the `penalizer` and `l1_ratio` are specified in the class creation:
+Both the ``penalizer`` and ``l1_ratio`` are specified in the class creation:
.. code:: python
