knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width=5, fig.height=5 ,fig.align="center" ) fpath <- ""
parsnip
is a R package that offers
a unified interface to many machine learning models.
By writing an interface between condvis2
and parsnip
a vast number
of machine learning fits may be explored with condvis
.
A list of models supported by parsnip is found on this link: https://www.tidymodels.org/find/parsnip/
Fit the regression model with parsnip.
library(parsnip) library(MASS) library(condvis2) Boston1 <- Boston[,9:14] fitlm <- linear_reg() %>% set_engine("lm") %>% fit(medv ~ ., data = Boston1) fitrf <- rand_forest(mode="regression") %>% set_engine("randomForest") %>% fit(medv ~ ., data = Boston1)
Use condvis to explore the models:
condvis(Boston1, model=list(lm=fitlm,rf=fitrf), response="medv", sectionvars="lstat")
Choose tour "Diff fits" to explore differences between the fits
Some tasks, for example linear regression, support confidence intervals.
Tell condvis
to plot an interval using pinterval="confidence
for that fit.
The forest fit does not support confidence intervals so the predictArgs for that fit are
NULL.
condvis(Boston1, model=list(lm=fitlm,rf=fitrf), response="medv", sectionvars="lstat", predictArgs=list(list(pinterval="confidence"), NULL))
Fit some classification models:
clmodel <- svm_poly(mode="classification") %>% set_engine("kernlab") %>% fit(Species ~ ., data = iris )
Explore with condvis
:
condvis(iris, model=clmodel, response="Species", sectionvars=c("Petal.Length", "Petal.Width"), pointColor="Species")
Click on "Show probs" to see class probabilities.
Fit a survival model and explore with condvis:
library(survival) # for the data smodel <- surv_reg() %>% set_engine("survival") %>% fit(Surv(time, status) ~ inst+age+sex+ph.ecog, data=lung) condvis(na.omit(lung), smodel, response="time", sectionvars = c("inst","sex"), conditionvars=c("age","ph.ecog"))
Unlike mlr
, parsnip
does not yet offer support for clustering fits.
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