DOC make notation for regression criterion more uniform and better looking
@@ -490,17 +490,17 @@ Mean Squared Error:
.. math::
- c_m = \frac{1}{N_m} \sum_{i \in N_m} y_i
+ \bar{y}_m = \frac{1}{N_m} \sum_{i \in N_m} y_i
- H(X_m) = \frac{1}{N_m} \sum_{i \in N_m} (y_i - c_m)^2
+ H(X_m) = \frac{1}{N_m} \sum_{i \in N_m} (y_i - \bar{y}_m)^2
Mean Absolute Error:
.. math::
- \bar{y_m} = \frac{1}{N_m} \sum_{i \in N_m} y_i
+ \bar{y}_m = \frac{1}{N_m} \sum_{i \in N_m} y_i
- H(X_m) = \frac{1}{N_m} \sum_{i \in N_m} |y_i - \bar{y_m}|
+ H(X_m) = \frac{1}{N_m} \sum_{i \in N_m} |y_i - \bar{y}_m|
where :math:`X_m` is the training data in node :math:`m`
