Classification: a level for the logit and exponential losses, and two fixes for tree families without a split - #121

The OOB path tracked every family's predictions from the score 0 its rows
start from, and added the leaf values of each split. A family that has made
no split predicts its null leaf's fit (fit_null_leaf(): the mean of its rows,
or for logit and exponential the log-odds of its sample), so the path held 0
where the forest with that many splits predicts that fit; for logit and
exponential a probability of 1/2. With a rare class and few rows many
families make no split in their first rounds, and the OOB log-loss of few
splits came out too high, so that the adaptive choice of n_splits could grow
far past the best number of splits.

path_start() now computes the null leaf's fit for every tracked family (the
adaptive fit did so already for its rebuild), path_split() marks a family
that has split, and path_snapshot() gives a family without a split at its
fit at every checkpoint, for the fixed fit's path and the adaptive one.
Families with a split are tracked as before, so forests whose families all
split by the first checkpoint are unchanged.

A forest with loss = "logit" or "exponential" averages its tree families on the
score scale. Where the families disagree, the probability of their mean score
lies below the mean of their probabilities for a rare class (the probability is
convex in the score below 1/2), the more so the more splits they make: the
forest predicted a rare class too rarely. After the fit the forest's score now
gets one constant per score column, its level: the maximum-likelihood intercept
of the log-loss given the forest's out-of-bag scores, whose equations are the
balance property (every class's mean out-of-bag probability is its frequency),
with the second-order term of the out-of-bag correction for an out-of-bag mean
of fewer families than the forest. classif_level() solves them by Newton's
method with step halving (3 to 6 steps). The level is added to every family's
null leaf (shift_level()), so predict(), purify(), the pruning's components and
saved models see it as part of the intercept; $level holds it and print() shows
it. deterministic = TRUE balances the forest's own predictions.

The adaptive choice of n_splits scores every checkpoint at its own level and the
forest is shifted by the chosen checkpoint's; a fit with fixed splits tracks its
OOB path at its last split for the level. A forest with a level is purified
after the shift; the pruning's cross-validation refits each fold with its own
level.

The level is on by default for both losses, binary and multiclass;
options(randomPlantedForest.level = FALSE) fits without it, bit for bit as
before. The setting is stored in $params$level, and a fit without it is refitted
without a level. Regression and the L1 / L2 classification losses are
unchanged. The tests of the losses' steps and of the OOB path's growth run
without the level; the pinned digests of the logit and exponential fits and the
print() snapshot of a deterministic logit fit are re-pinned. The classification
article gains a short section on the level, and its table of a pure leaf's
steps is fitted without it.

classif_level() built its Newton step with crossprod() and solve(), i.e. BLAS and
LAPACK, and summed with colSums(), rowSums() and sum(), which accumulate in long
double. The step's last bits then depend on the library: OpenBLAS's triangular
solve multiplies by the reciprocal of the pivot where the reference BLAS divides
by it. With R's reference BLAS (CRAN's macOS R) the level of two pinned fits,
cls_cur_trees_2 and loss3_exponential, came out a few ulps away from OpenBLAS's
(Homebrew R), so test-fit-digests.R failed on GitHub's macOS runner.

The level's sums are now pairwise sums in double arithmetic (level_colsums(),
level_sum()) or column by column (level_rowsums()), the Jacobian is summed term
by term, and the step is solved by Gaussian elimination with the diagonal as
pivots (level_solve(); the Jacobian is positive definite). Nothing in the level
calls BLAS or LAPACK any more, and its bits depend only on its inputs and on
exp() and log().

The levels move at rounding level (at most 5e-15 in the 52 digests' fits, the
same number of Newton steps), so the 13 digests of the logit and exponential
fits are re-pinned.