DOC Fixing summary table in the linear model documentation.
@@ -775,20 +775,29 @@ The "saga" solver [7]_ is a variant of "sag" that also supports the
non-smooth `penalty="l1"` option. This is therefore the solver of choice
for sparse multinomial logistic regression.
-In a nutshell, the following table summarizes the solvers characteristics:
-
-============================ =========== ======= =========== ===== ======
-solver 'liblinear' 'lbfgs' 'newton-cg' 'sag' 'saga'
-============================ =========== ======= =========== ===== ======
-Multinomial + L2 penalty no yes yes yes yes
-OVR + L2 penalty yes yes yes yes yes
-Multinomial + L1 penalty no no no no yes
-OVR + L1 penalty yes no no no yes
-============================ =========== ======= =========== ===== ======
-Penalize the intercept (bad) yes no no no no
-Faster for large datasets no no no yes yes
-Robust to unscaled datasets yes yes yes no no
-============================ =========== ======= =========== ===== ======
+In a nutshell, the following table summarizes the penalties supported by each solver:
+
++------------------------------+-----------------+-------------+-----------------+-----------+------------+
+| | **Solvers** |
++------------------------------+-----------------+-------------+-----------------+-----------+------------+
+| **Penalties** | **'liblinear'** | **'lbfgs'** | **'newton-cg'** | **'sag'** | **'saga'** |
++------------------------------+-----------------+-------------+-----------------+-----------+------------+
+| Multinomial + L2 penalty | no | yes | yes | yes | yes |
++------------------------------+-----------------+-------------+-----------------+-----------+------------+
+| OVR + L2 penalty | yes | yes | yes | yes | yes |
++------------------------------+-----------------+-------------+-----------------+-----------+------------+
+| Multinomial + L1 penalty | no | no | no | no | yes |
++------------------------------+-----------------+-------------+-----------------+-----------+------------+
+| OVR + L1 penalty | yes | no | no | no | yes |
++------------------------------+-----------------+-------------+-----------------+-----------+------------+
+| **Behaviors** | |
++------------------------------+-----------------+-------------+-----------------+-----------+------------+
+| Penalize the intercept (bad) | yes | no | no | no | no |
++------------------------------+-----------------+-------------+-----------------+-----------+------------+
+| Faster for large datasets | no | no | no | yes | yes |
++------------------------------+-----------------+-------------+-----------------+-----------+------------+
+| Robust to unscaled datasets | yes | yes | yes | no | no |
++------------------------------+-----------------+-------------+-----------------+-----------+------------+
The "saga" solver is often the best choice. The "liblinear" solver is
used by default for historical reasons.
