Update cuML benchmark utility (add LinearSVC and improve Random Forest fairness)

[beckernick]

This PR makes several small changes:

Adds LinearSVC to the cuML benchmarks. Currently, we run SVC(linear) to benchmark a linear SVM. The scikit-learn documentation recommends using LinearSVC for large datasets instead for performance reasons. For even 10,000 records, the performance difference is quite significant. As the model quality can differ slightly between SVC(linear) and LinearSVC, we add LinearSVC rather than replace SVC(linear).

from sklearn.datasets import make_classification
from sklearn.svm import LinearSVC, SVC

X, y = make_classification(n_samples=30000, n_features=10)

clf = LinearSVC()
%time clf.fit(X,y)
print(clf.score(X,y))

clf = SVC(kernel="linear")
%time clf.fit(X,y)
print(clf.score(X,y))
CPU times: user 529 ms, sys: 4.09 ms, total: 534 ms
Wall time: 534 ms
0.9278
CPU times: user 5.23 s, sys: 115 ms, total: 5.35 s
Wall time: 5.35 s
0.9278

Updates RandomForest{Classifier, Regressor} to use all CPU cores on the machine and to train more than 10 trees. The scikit-learn implementation benefits significantly from using multiple cores, but the benefit is capped by the number of trees. On large machines, using only 10 trees will bias toward slower performance relative to what's possible. As it's rare for people to train Random Forests with only 10 trees, this is changed to a more reasonable (but small) number of 50 trees.

clf = RandomForestClassifier(n_estimators=2, n_jobs=1)
%time clf.fit(X,y)

clf = RandomForestClassifier(n_estimators=2, n_jobs=-1)
%time clf.fit(X,y)

clf = RandomForestClassifier(n_estimators=6, n_jobs=-1) # three times as many trees, same wall time
%time clf.fit(X,y)
CPU times: user 3.09 s, sys: 20.9 ms, total: 3.11 s
Wall time: 3.1 s
CPU times: user 3.14 s, sys: 8.51 ms, total: 3.14 s
Wall time: 1.76 s
CPU times: user 8.74 s, sys: 19.3 ms, total: 8.76 s
Wall time: 1.68 s

Updates RandomForestClassifier to use max_features="sqrt" rather than 1.0. This is generally regarded as the appropriate default setting (used in scikit-learn and noted in Hastie's ESL). Using 1.0 as max features takes significantly longer to train on the CPU and results in more correlated trees, which is not expected to improve results. As a result, it's not the ideal "default" characterization of performance.

[codecov-commenter]

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[beckernick]

Superseded by #5242