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vignettes/h2o.Rmd now shuts down the H2O cluster it
starts, so the Java process it spawns no longer outlives the vignette
build (leftover processes were flagged on CRAN’s check systems).Added support for the following model classes, and for the parsnip model types and engines that fit them.
mboost::blackboost() gradient boosting, via
boost_tree() with the "mboost" engine. (#249)
Gaussian() family is supported.dbarts::bart() Bayesian additive regression trees, via
bart() with the "dbarts" engine. (#268)
keeptrees = TRUE, and only
continuous outcomes are supported since binary outcomes are fit with a
probit link.C50::C5.0() classification trees, including rule-based
ones (rules = TRUE), via decision_tree(),
boost_tree() and C5_rules() with the
"C5.0" engine. (#245, #248, #251)
trials > 1) combine trials by
confidence-weighted voting, but boosted rule-based models are not
supported.fuzzyThreshold = TRUE) and cost
matrices (costs) are not supported.xrf::xrf() rule-based models (RuleFit), via
rule_fit() with the "xrf" engine. (#256)
family = "gaussian") and binary
classification (family = "binomial") only; multinomial
models are not supported.MASS::lda() linear discriminant analysis, via
discrim_linear() with the "MASS" engine.
(#258)MASS::qda() quadratic discriminant analysis, via
discrim_quad() with the "MASS" engine.
(#271)mda::fda() flexible discriminant analysis, via
discrim_linear() with the "mda" engine. (#259)
mda::polyreg() with
degree = 1, or mda::gen.ridge().sda::sda() shrinkage discriminant analysis, via
discrim_linear() with the "sda" engine.
(#260)sparsediscrim (lda_diag(),
lda_shrink_mean(), lda_shrink_cov() and
lda_emp_bayes_eigen()), via discrim_linear()
with the "sparsediscrim" engine. (#261)partykit::cforest() random forests, via
rand_forest() with the "partykit" engine.
(#246)
aorsf::orsf() oblique random forests, via
rand_forest() with the "aorsf" engine. (#247)
baguette::bagger() bagged tree ensembles fit with the
"CART" or "C5.0" base model, via
bag_tree() with the "rpart" or
"C5.0" engine. (#269, #270)H2ORegressionModel,
H2OBinomialModel and H2OMultinomialModel), via
boost_tree() with the "h2o_gbm" engine. (#250)
h2o::h2o.rulefit()), via
rule_fit() with the "h2o" engine. (#257)
Both are supported when fit without kernel density estimates
(usekernel = FALSE), and return a named list of
class-probability expressions.
klaR::NaiveBayes() with Gaussian densities, via
naive_Bayes() with the "klaR" engine.
(#264)naivebayes::naive_bayes(), via
naive_Bayes() with the "naivebayes" engine.
(#266)
nnet::nnet() single hidden layer neural networks, via
mlp() with the "nnet" engine. (#267)nnet::multinom() multinomial log-linear models, via
multinom_reg() with the "nnet" engine.
(#255)kernlab::ksvm() linear support vector machines
(vanilladot kernel), via svm_linear() with the
"kernlab" engine, for regression and binary classification.
(#252)
prob.model = TRUE).LiblineaR::LiblineaR() support vector machines, via
svm_linear() with the "LiblineaR" engine, for
regression (type 11, 12, 13) and binary classification
(type 1-5). (#253)
LiblineaR::LiblineaR() binary logistic regression
(type 0, 6, 7), via logistic_reg() with the
"LiblineaR" engine. (#243)mixOmics
(pls(), spls(), plsda() and
splsda()), via pls() with the
"mixOmics" engine, for regression and classification.
(#262)quantreg::rq() quantile regression, via
linear_reg() with the "quantreg" engine.
(#241)parsnip::nullmodel(), via null_model()
with the "parsnip" engine. (#263)Added support for multinomial glmnet::glmnet()
models (family = "multinomial"), including
multinom_reg() parsnip models fitted with the
"glmnet" engine. tidypredict_fit() returns a
named list of class-probability expressions (softmax). (#198,
#254)
Added support for decision_tree() parsnip models
fitted with the "rpart" engine. (#244)
Added support for linear_reg() parsnip models fitted
with the "glm" engine. (#239)
tidypredict_fit() now supports
C50::C5.0() models that split a discrete predictor into one
branch per level. (#245)
tidypredict_save() and tidypredict_load()
write a parsed model to a YAML file and read it back. Use them instead
of yaml::write_yaml(), which stores only 7 significant
digits by default and so rounds split thresholds enough to send rows
down a different branch when the model is re-loaded. (#307)acceptable_formula(), parse_model(),
tidypredict_fit() and tidypredict_interval()
now report a model class they do not support with a message naming the
class, rather than failing with R’s “no applicable method” error.
tidypredict_interval() gives a parsed model the same
message it gives a fitted one, instead of “Model type not supported.”.
(#313)
as_parsed_model() now rejects an object that is not
a parsed model. (#313)
tidypredict_interval() now rejects an
interval that is not a single number strictly between 0 and
1.(#313)
tidypredict_interval() now reports a list that is
not a parsed model, (#308, #313)
tidypredict_sql() and
tidypredict_sql_interval() now check that dbplyr is
installed before using it. (#314)
tidypredict_to_column() now validates
vars, add_interval and interval.
(#313)
tidypredict_to_column() now explains that a model
returning more than one formula is unsupported, instead of incorrectly
claiming that tree based models are unsupported. (#279)
New articles for kernlab::ksvm(),
mboost::blackboost() and xrf::xrf(), and the
model list menu now links to the LiblineaR and
quantreg sections directly. (#317)
The lm, glm, ranger, randomForest and rpart articles recommended
tidypredict_to_sql(), which does not exist. They now point
at tidypredict_sql(). (#317)
The Cubist article now documents two limits on how closely
tidypredict_fit() can match Cubist::predict().
The instance-based correction that predict() applies when
neighbors is greater than zero is not reproduced, because
it adjusts each prediction using training rows that are not part of the
fitted model. Separately, Cubist stores its coefficients as 32-bit
floats, so the agreement has a relative ceiling near 1e-7 rather than an
absolute one, and an outcome on a large scale leaves a proportionally
large absolute difference. (#375)
The glm article now documents the one inverse link
tidypredict_fit() does not reproduce exactly:
probit, whose inverse is pnorm(), is written
as the Bowling et al. logistic approximation to the normal CDF because
no SQL backend has a normal CDF. It costs about 1e-4 of probability.
(#355)
The naive Bayes article now documents the one case where
tidypredict_fit() does not reproduce predict()
for klaR::NaiveBayes() and
naivebayes::naive_bayes() models: both replace a normal
density that underflowed to zero with their threshold
argument, which takes a value roughly 38 standard deviations from the
class mean, and the log scale used throughout never underflows.
(#300)
The models article now documents a limit on
kernlab::ksvm() models fitted through the matrix interface,
ksvm(x, y). ksvm() mangles its model matrix
column names with make.names() and keeps no record of the
originals, and unlike the formula interface there is no
terms object to detect this against, so a non-syntactic
column name such as a:b yields a formula referring to a
column the data does not have. This cannot be caught automatically,
because every name make.names() produces is also a name it
leaves alone, so a.b from a mangled a:b is
indistinguishable from a correct model with a column genuinely named
a.b. (#418)
.build_case_when_tree(), which {orbital} calls, now
returns the bare prediction of a classification stump, instead of the
case_when(.default = "a") that dplyr rejects.
(#310)
acceptable_formula() now checks the contrast of
every factor predictor, not just one, and names the offending field. A
model mixing contrasts was accepted and then silently mis-parsed.
(#291)
acceptable_formula() no longer rejects a
MASS::lda(), MASS::qda() or
earth::earth() fit whose factor has a level containing a
colon, which the contrast check read as an interaction. (#391)
set_catboost_categories() now takes its hashes from
CatBoost’s own hash function, so it names every category of any factor.
A factor with four or more levels errored with “No category mapping
found for hash”, and a two-level one could be named the wrong way round.
(#297)
tidypredict_fit() now works on a LightGBM model
whose trees are bare leaves, which failed with “Model has no trees.”. A
multiclass model with only some bare leaves also assigned trees to the
wrong classes. (#401)
tidypredict_fit() now returns one prediction per row
for a ranger::ranger(), xgboost,
baguette::bagger() or xrf::xrf() model whose
formula mentions no column, such as an all-stump forest or an
intercept-only lasso. The value was correct, its length was not.
(#397)
tidypredict_fit() now applies the bias correction of
a randomForest::randomForest() model fitted with
corr.bias = TRUE, which left predictions off by as much as
0.21 for a model of mpg on mtcars.
(#395)
tidypredict_fit() now sends a split threshold that
is not finite, or that overflows the 32-bit float range, down the branch
the model does, rather than turning it into a NaN that
fails every comparison. (#313)
tidypredict_fit() now works on a parsed LightGBM
model fit with linear_tree = TRUE, which failed with
“..1 (right) must be a vector, not NULL”.
(#346)
tidypredict_fit() now follows the per-node missing
value direction a ranger::ranger() model learns under the
na.action = "na.learn" default of ranger 0.17.0, instead of
always sending them left. (#394)
tidypredict_fit() now matches predict()
for a ranger::ranger() model fitted with
respect.unordered.factors = "partition" on a factor with
more than 31 levels, whose split mask ranger::treeInfo()
silently blanks out. (#414)
tidypredict_fit() no longer returns
NULL for a parsed model saved by tidypredict 1.0.1 or
earlier that came from a partykit or rpart
single tree. Any parsed model type that is still unhandled now raises an
error. (#304)
tidypredict_fit() no longer fails with
“x must be a formula” on a parsed model saved by
tidypredict 1.0.1 or earlier that contains a
ranger::ranger() or
randomForest::randomForest() stump. (#310)
tidypredict_fit() now handles three parsed model
shapes that only a hand-written or edited parsed model contains: a path
mixing a type = "all" element with real conditions, a rule
whose linear prediction is a single non-intercept term, and a rule whose
terms are all zero. (#310)
tidypredict_fit() now assigns rules to the right
committee for Cubist::cubist() models fitted with more than
20 committees, where the printed model it read the counts from truncates
them. (#286)
tidypredict_fit() now applies the per-rule
extrapolation limits of Cubist::cubist() models, which hold
each rule to the span of the training outcomes it covers. This engages
on rows of the training data too. (#285)
tidypredict_fit() now supports factor predictors for
Cubist::cubist() models, which previously produced a
formula that could not be evaluated
(object '"f"' not found). (#322)
tidypredict_fit() now reads the coefficient labels
of an lm(), glm() or
quantreg::rq() model from the model’s own term structure. A
factor level containing a : was taken apart as an
interaction, and a label equal to another predictor’s name was read as
that predictor. (#308)
tidypredict_fit() now rejects an
earth::earth() model fit with a contrast other than the
treatment one. An ordered factor gave a formula comparing the column
against contrast values such as -0.2236. (#323)
tidypredict_fit() now routes missing values by each
node’s missing_type for lightgbm models.
Consulting default_left alone was wrong for every model
trained without missing data, which is the common case. (#288)
tidypredict_fit() now honors
zero_as_missing for lightgbm models, where an
exact zero takes the same branch as a missing value. Predictions were
wrong on the training data itself. (#288)
tidypredict_fit() no longer sends a missing value
down the left branch of a categorical split for lightgbm
models. LightGBM sends it right whatever default_left says.
(#288)
tidypredict_fit() no longer returns NaN
for every class probability of a row whose class scores are large, for
any model whose prediction is a softmax.
exp(s) / sum(exp(s)) overflows once a score passes about
710, and is now written as the equivalent
1 / sum(exp(s_j - s_k)). (#299)
tidypredict_fit() now rejects a glmnet
model fit with an offset, whose values glmnet never
records, rather than silently dropping it and predicting wrong by its
size. (#296)
tidypredict_fit() now rejects a
ranger::ranger() probability or survival forest, read from
treetype, instead of emitting
case_when(x <= 0.0066 ~ NULL, .default = NULL).
(#301)
tidypredict_fit() now sends a value sitting exactly
on a split boundary the way the model does, for the backends that
compare split thresholds as 32-bit floats: xgboost,
lightgbm, catboost,
Cubist::cubist() and C50::C5.0(). About half
of all thresholds round that tie towards the neighbouring float.
(#350)
tidypredict_fit() now honors sigmoid
for lightgbm models fit with the binary or
multiclassova objective. Every probability of a model fit
with any other value was rescaled. (#288)
tidypredict_fit() now honors reg_sqrt
for lightgbm models, whose predictions were left on the
square-root scale. (#288)
tidypredict_fit() and parse_model() now
work on an xgboost booster that has been saved and reloaded
with xgb.save() / xgb.load(), which failed
with argument "model" is missing, with no default. The
objective is now recovered from the saved model, which a reloaded
booster records nowhere else. (#292)
tidypredict_fit() now works for rank-deficient
lm() and glm() models, which aborted with
“Unable to calculate inverse of QR decomposition”. A duplicated
predictor column or one with no variance is enough to hit it, and
tidypredict_interval() keeps working for these models.
(#308)
tidypredict_fit() now supports splits with more than
two branches for partykit models, such as those from
ctree_control(multiway = TRUE). Every branch after the
second was dropped. (#295)
tidypredict_fit() now honors
partysplit(right = FALSE) for partykit models,
where a value falling exactly on the break took the wrong branch.
(#295)
tidypredict_fit() now handles ordered factor
predictors for partykit models, which previously errored
with “Result must be length 1, not 2”. (#295)
tidypredict_fit() no longer swaps the two branches
of every partykit::party converted from an
rpart model. (#295)
tidypredict_fit() now decodes factor splits for
ranger::ranger() models, in all three
respect.unordered.factors modes and for ordered factors,
rather than comparing the split value as a numeric threshold.
(#283)
tidypredict_fit() now decodes factor splits for
randomForest::randomForest() models, where an unordered
factor’s split point is a bit mask and an ordered factor’s is a level
code, rather than reading either as a numeric threshold. (#282)
tidypredict_fit() and parse_model() now
handle a stump in a randomForest::randomForest() forest,
instead of aborting with “argument of length 0”. A constant outcome or a
zero-variance predictor makes one routine. (#362)
tidypredict_fit() now substitutes the training mean
for a missing predictor in Cubist::cubist() models,
matching predict(), in the rule conditions as well as the
linear models. (#294)
tidypredict_fit() now sends a missing predictor down
the left branch for ranger::ranger() models, matching
predict(). (#294)
tidypredict_fit() now routes missing values through
surrogate splits for rpart::rpart() models, and for
baguette::bagger() models using the "CART"
base model, in all three usesurrogate modes, instead of
sending them right. (#294)
tidypredict_fit() now returns NA for a
row that reaches a split on a predictor it is missing, for
partykit::ctree(), partykit::cforest() and
mboost::blackboost() models. These resolve a missing value
by random sampling, so predict() returns a different answer
on each call. (#294)
tidypredict_fit() now returns NA for a
row with a missing predictor for
randomForest::randomForest() and aorsf::orsf()
models, neither of which will predict from an incomplete row. Rows are
kept rather than dropped. (#294, #325)
tidypredict_fit() now returns correct predictions
for catboost models whose predictor values fall on a split
border, which catboost compares as 32-bit floats. (#298)
tidypredict_fit() now picks the right factor
predictor when three or more variable names are nested prefixes of one
another, such as x, xy and xyz.
The wrong variable was silently chosen for lm(),
glm(), quantreg::rq(),
nnet::multinom(), nnet::nnet() and
earth::earth(). (#290)
tidypredict_fit() now uses a strict inequality
(<) for the continuous splits of
rpart::rpart() models, matching how rpart
assigns values exactly equal to a cut point. (#232)
tidypredict_fit() now returns correct predictions
for randomForest::randomForest() models saved and reloaded
with parse_model() and as_parsed_model(),
which named every split variable after the first leaf of a tree
incorrectly. (#232)
tidypredict_fit() now returns correct predictions
for Cubist::cubist() models whose predictor values fall
exactly on a split threshold, which Cubist compares as 32-bit floats.
(#232)
tidypredict_fit() now keeps small probabilities for
models with a logit link, such as glm() with
family = binomial and LiblineaR::LiblineaR().
The inverse link rounded to exactly 0 once the linear predictor fell
below about -37. (#232)
tidypredict_fit() now returns correct predictions
for xgboost models whose feature values fall exactly on a split
threshold, which xgboost compares as 32-bit floats. (#45)
tidypredict_fit() now returns correct predictions
for xgboost models that have been saved and reloaded with
parse_model() and as_parsed_model().
Previously every tree collapsed to a single leaf value. (#232)
tidypredict_interval() now works for
glm() models, which returned numeric(0) for
every gaussian glm because the residual variance was read from
summary()$sigma, which only summary.lm() has.
(#293)
tidypredict_interval() now honors its
interval argument, which was hardcoded to 0.95.
tidypredict_to_column(add_interval = TRUE) and
tidypredict_sql_interval() were affected too.
(#232)
tidypredict_sql() now returns a single query for an
intercept-only model, whose bare-number formula was mistaken for the
list a multiclass model produces. (#313)
tidypredict_fit() now returns predictions on the
response scale for CatBoost models fit with the Poisson or
Tweedie objective, applying exp() to the raw
score as the other CatBoost objectives already invert their own links.
Anyone using such a model will see their predictions change from the log
scale to the count or mean scale; they now match
catboost.predict(prediction_type = "Exponent") instead of
the "RawFormulaVal" default. (#356)New generics expose the pieces tidypredict_fit() is
assembled from, so that packages generating their own code from a fitted
model can reuse tidypredict’s parsing: tidypredict_trees()
returns per-tree expressions, tidypredict_class_trees()
returns them for each outcome level,
tidypredict_class_exprs() returns one finished expression
per outcome level, and tidypredict_n_trees() returns the
number of trees. See ?tidypredict_extractors.
(#433)
tidypredict_combine_trees() is a new generic that
turns per-tree expressions back into a model’s prediction, with methods
for randomForest, ranger, xgboost, LightGBM,
CatBoost, cforest, blackboost and
aorsf. Summing or averaging the trees, as the shape of the
list invites, is wrong for any backend carrying an offset, a scale or a
link. (#436)
tidypredict_trees() and
tidypredict_n_trees() gain methods for
partykit::cforest(), mboost::blackboost() and
aorsf::orsf(). (#436)
Boosted C50::C5.0() models deliberately have no
tidypredict_trees() method, and
tidypredict_combine_trees() refuses them with an
explanation. Their trials vote with a class label and a confidence
rather than contributing numbers, so there is nothing to sum or average.
(#436)
?tidypredict_extractors now documents which model
classes implement each of the extractor generics, and what to implement
when adding a new one. The three seam topics cross-reference each other.
(#436)
New generics describe what a model’s fitted expressions compute,
which the expressions themselves do not say:
tidypredict_output_type() returns one of
"numeric", "prob", "decision" or
"class", tidypredict_outcome_levels() returns
the outcome levels in model order, and
tidypredict_normalized() reports whether per-level
probabilities already sum to one. None of it is recoverable from the
shape of the result: a LiblineaR SVM classifier and a
LiblineaR logistic regression both return a single
expression, but only the second is a probability. See
?tidypredict_metadata. (#433, #435)
tidypredict_class_exprs() on a partykit
model is named by outcome level. The
.extract_partykit_classprob() it replaces returned an
unnamed list, which left callers assuming its order matched
levels() of the outcome. (#433)
The error raised when no method knows how to handle a model at
all now carries the condition class
tidypredict_unsupported_model, so a wrapper such as orbital
can tell it apart from the many errors reporting an unsupported
configuration of a model that is otherwise handled.
(#432)
parse_model() and tidypredict_fit() now
detect xgboost dropout (DART) models from the saved dropout weights
rather than the serialised booster name, so they keep applying
weight_drop with xgboost 3.4.0 and later, which
canonicalises booster = "dart" to "gbtree".
(#238)Added support for rpart decision tree models
(rpart). (#226)
Added support for CatBoost models (catboost.Model).
(#179, #187, #188)
SymmetricTree) and
non-oblivious (Depthwise or Lossguide grow
policy).set_catboost_categories().Added support for LightGBM models (lgb.Booster).
(#177, #186)
linear_tree = TRUE), which fit a
linear model at each leaf instead of a constant.Tree models (rpart, partykit, ranger, randomForest, xgboost,
lightgbm, catboost) now generate nested case_when()
expressions that mirror the tree structure, instead of flat expressions
with all leaf conditions at the same level. This produces more efficient
SQL and R code because conditions are evaluated hierarchically.
(#227)
parse_model() now documents the parsed model version
system (v1/v2/v3) and model type classes in its help page.
(#227)
earth() models now support additional GLM families
and link functions: Gamma, inverse.gaussian, probit, and cloglog. (#194,
#195)
glm() models now support additional families and
link functions: Gamma family with inverse link, inverse.gaussian family
with 1/mu^2 link, probit link, cloglog link, and sqrt link. (#203, #204,
#205, #206, #207)
glmnet() models now support Gamma
family and Cox proportional hazards (family = "cox")
models. (#200, #201)
xgboost support now includes additional objectives:
binary:hinge, reg:absoluteerror,
reg:gamma, reg:pseudohubererror, and
reg:squaredlogerror. (#184)
Added a vignette on floating-point precision issues with tree-based models. (#231)
tidypredict_fit() now correctly handles xgboost
models with stump trees (single leaf, no splits). (#182)
tidypredict_fit() now correctly handles xgboost DART
booster models with rate_drop > 0. DART uses tree weight
normalization during training, and these weights are now properly
applied to each tree’s predictions. (#183)
tidypredict_fit() now correctly incorporates
base_score for xgboost models with
count:poisson and reg:tweedie objectives.
Previously, predictions were incorrect when base_score was
not the default value. (#184)
tidypredict_fit() now correctly averages tree
predictions for LightGBM models with boosting="rf" instead
of summing them. (#185)
tidypredict_fit() now uses the correct split
operator (<= instead of <) for ranger
models. Previously, predictions were incorrect when data values exactly
matched split values. (#189)
tidypredict_fit() now correctly averages tree
predictions for ranger models instead of summing them. Previously,
predictions were num.trees times too large. (#190)
tidypredict_fit() now throws a clear error for
ranger and randomForest classification models, which are not supported.
(#191, #193)
tidypredict_fit() now uses the correct split
operator (<= instead of <) for
randomForest models. (#192)
tidypredict_fit() now correctly handles partykit
stump trees (models with no splits). (#196)
tidypredict_fit() now works with
glmnet() models that use family function syntax (e.g.,
family = gaussian()) instead of string syntax (e.g.,
family = "gaussian"). (#197)
tidypredict_fit() now works with models that use
family function syntax (e.g., family = gaussian()) instead
of string syntax (e.g., family = "gaussian").
(#202)
base_score wasn’t extracted correctly
xgboost for version 3 or higher. (#173)xgboost models with objectives "reg:tweedie" and
"count:poisson" are now supported. (#72, @SimonCoulombe)
tree based models now uses .default argument in
produced case_when() code when applicable. (#153)
Speed up tidypredict_fit() for partykit and ranger
packages. (#125)
Speed up tidypredict_fit() for xgboost models.
(#130)
randomForest models now support regression outcomes. (#77)
An informative error will now be thrown if a lm model cannot be processed due to having linear combinations of predictors. (#124)
linear models such as lm() and glm()
now work with interactions created with * and
:. (#74)
Cubist rules will return simplified rules whenever possible to avoid multiplying by 0 and 1. (#152)
Make work with xgboost version > 2.0.0.0. (#169)
Fixed a bug where the intercept was added incorrectly to the result for cubist models. (#58)
Fixed bug where tidypredict would error on Cubist models without conditions. (#127)
Fixed bug where Cubist models incorrectly combined rules and committees. (#134)
Changes maintainer to Edgar Ruiz
Updates author’s email addresses.
Removes dependency with stringr
Fixes issue with earth parsed_models (#108)
Addresses issues with XGBoost models
Improvements to XGBoosts tests
Fixes issue handling GLM Binomial earth models (#97)
Adds capability to handle single simple Cubist models (#57)
Fixed parenthesis issue in the creation of the interval formula (#76)
Fixed bug in SQL query generation for XGBoost models with
objective binary:logistic.
Re-licensed package from GPL-3 to MIT. See consent from copyright holders here.
earth package. As a result, tidypredict will
only parse objects created by this and later versions of
earth.xgboost changes.Adds support for categorical predictors in
partykit
Fixes parsnip tests to meet standards of new CRAN
version
Simplifies tests that verify ranger
Adds fit method for parsed xgboost models
Sets conditional requirement for xgboost, for test
and vignette
Parses ranger classification models.
Adds method support for broom’s tidy()
function. Regression models only
Adds as_parsed_model() function. It adds the proper
class components to the list.
Adds initial support for partykit’s
ctree() model
Adds support for parsnip fitted models:
lm, randomForest, ranger, and
earth
Adds support for xgb.Booster models provided by the
xgboost package (@Athospd, #43)
Adds support for Cubist::cubist() models (#
36)
earth
packageNew parsed models are now list objects as opposed to data frames.
tidypredict_to_column() no longer supports ranger
and randomForest because of the multiple queries generated
by multiple trees.
All functions that read the parsed models and create the tidy eval formula now use the list object.
Most of the code that depends on dplyr programming has been removed.
Removes dependencies on: tidyr, tibble
The x/y interface for earth models can
now be used.
randomForest & ranger) (#29)ranger() models.x ~. in a randomForest() formula fails (#18 @washcycle).These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.