Log loss metric can be Infinity or NaN

For binary classification (and perhaps multiclass classification) the `log loss` can be infinite. The `log loss reduction` can also be negative infinity, as it is a shifting and rescaling of the `log loss`.

Similarly, the `log loss` can be a `NaN`. This is specifically guarded against in the code, but does seems like a bug too.

The culprit for both cases lies in the initial calculations in the `ProcessRow()` method of the `Aggregator` for the `BinaryClassifierEvaluator`.

```cs
Double logloss;
if (!Single.IsNaN(prob))
{
if (_label > 0)
{
// REVIEW: Should we bring back the option to use ln instead of log2?
logloss = -Math.Log(prob, 2);
}
else
logloss = -Math.Log(1.0 - prob, 2);
}
else
logloss = Double.NaN;
```

I propose that to guard against infinities we add an epsilon before taking the log.

To guard against `NaNs`, we will need to fix the probability calculations (e.g. in the calibrator(s)).