DOC Small fixes in Tweedie deviances documentation
@@ -2375,10 +2375,10 @@ is defined as
\sum_{i=0}^{n_\text{samples} - 1}
\begin{cases}
(y_i-\hat{y}_i)^2, & \text{for }p=0\text{ (Normal)}\\
- 2(y_i \log(y/\hat{y}_i) + \hat{y}_i - y_i), & \text{for }p=1\text{ (Poisson)}\\
+ 2(y_i \log(y_i/\hat{y}_i) + \hat{y}_i - y_i), & \text{for }p=1\text{ (Poisson)}\\
2(\log(\hat{y}_i/y_i) + y_i/\hat{y}_i - 1), & \text{for }p=2\text{ (Gamma)}\\
2\left(\frac{\max(y_i,0)^{2-p}}{(1-p)(2-p)}-
- \frac{y\,\hat{y}^{1-p}_i}{1-p}+\frac{\hat{y}^{2-p}_i}{2-p}\right),
+ \frac{y_i\,\hat{y}_i^{1-p}}{1-p}+\frac{\hat{y}_i^{2-p}}{2-p}\right),
& \text{otherwise}
\end{cases}
@@ -2390,8 +2390,8 @@ distribution (``power=0``), quadratically. In general, the higher
``power`` the less weight is given to extreme deviations between true
and predicted targets.
-For instance, let's compare the two predictions 1.0 and 100 that are both
-50% of their corresponding true value.
+For instance, let's compare the two predictions 1.5 and 150 that are both
+50% larger than their corresponding true value.
The mean squared error (``power=0``) is very sensitive to the
prediction difference of the second point,::
