knitr::opts_chunk$set(echo = TRUE)
models <- c("glm", "lda", "naive_bayes", "svmLinear", "knn", "gamLoess", "multinom", "qda", "rf", "adaboost")
library(caret)
## Loading required package: lattice
## Loading required package: ggplot2
library(dslabs)
set.seed(1) # use `set.seed(1, sample.kind = "Rounding")` in R 3.6 or later
data("mnist_27")
fits <- lapply(models, function(model){
print(model)
train(y ~ ., method = model, data = mnist_27$train)
})
## [1] "glm"
## [1] "lda"
## [1] "naive_bayes"
## [1] "svmLinear"
## [1] "knn"
## [1] "gamLoess"
## Loading required package: gam
## Loading required package: splines
## Loading required package: foreach
## Loaded gam 1.16.1
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.53846
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.53555
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.57895
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.53555
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.46667
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.43969
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.53846
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.53555
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.57895
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.53555
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.46667
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.43969
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.53846
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.50205
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.51111
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.50205
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.57895
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.50205
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.53333
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.50205
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.50588
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.50205
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.089286
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : lowerlimit 0.092316
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.53846
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.53555
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.57895
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.53555
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.46667
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.43969
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.57895
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.54071
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.089286
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : lowerlimit 0.092316
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.53846
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.51322
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.57895
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.51322
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.53333
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.51322
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.57895
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.54071
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.57895
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.54071
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.46667
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.43969
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.089286
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : lowerlimit 0.10703
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.094737
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : lowerlimit 0.10703
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.089286
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : lowerlimit 0.10723
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.094737
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : lowerlimit 0.10723
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.57895
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.54061
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.46667
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.43969
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.53846
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.53555
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.57895
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.53555
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.53846
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.51322
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.57895
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.51322
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.53333
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.51322
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.089286
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : lowerlimit 0.092316
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.46667
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.43969
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.089286
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : lowerlimit 0.10703
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.094737
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : lowerlimit 0.10703
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.57895
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.54071
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.46667
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.40628
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.41379
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.40628
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.4375
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.40628
## Warning in gam.lo(data[["lo(x_1, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.089286
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : lowerlimit 0.10761
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.094737
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : lowerlimit 0.10761
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : eval 0.57895
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : upperlimit 0.54061
## Warning in gam.lo(data[["lo(x_2, span = 0.5, degree = 1)"]], z, w, span =
## 0.5, : extrapolation not allowed with blending
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts
## argument ignored
## [1] "multinom"
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 340.447413
## final value 340.447361
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 385.009822
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 340.515756
## final value 340.515703
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 355.491548
## final value 355.334096
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 399.994557
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 355.560397
## final value 355.404046
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 332.977945
## final value 332.776189
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 383.652420
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 333.057166
## final value 332.857086
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 360.815668
## iter 10 value 360.815664
## iter 10 value 360.815664
## final value 360.815664
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 401.991029
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 360.877678
## iter 10 value 360.877674
## iter 10 value 360.877673
## final value 360.877673
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 353.445113
## final value 353.445099
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 397.620330
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 353.512471
## final value 353.512457
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 384.705847
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 419.872138
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 384.756753
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 376.311956
## final value 376.311945
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 414.533042
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 376.368612
## final value 376.368601
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 348.115740
## final value 348.115719
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 394.780931
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 348.188604
## final value 348.188583
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 371.294670
## final value 371.294648
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 407.162776
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 371.346769
## final value 371.346747
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 340.460748
## final value 340.460735
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 385.329076
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 340.529383
## final value 340.529370
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 369.071134
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 409.102586
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 369.131201
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 353.789756
## final value 353.789729
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 397.213336
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 353.855907
## final value 353.855880
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 349.900015
## final value 349.900003
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 395.063948
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 349.969829
## final value 349.969816
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 340.283330
## final value 340.113998
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 388.970784
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 340.356087
## final value 340.192140
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 356.142461
## iter 10 value 356.142460
## iter 10 value 356.142460
## final value 356.142460
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 400.364737
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 356.210409
## iter 10 value 356.210409
## iter 10 value 356.210408
## final value 356.210408
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 366.460859
## final value 366.457990
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 408.726455
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 366.525723
## final value 366.522943
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 345.681221
## final value 345.681216
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 391.157784
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 345.751777
## final value 345.751772
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 381.951887
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 417.689638
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 382.004339
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 374.392494
## iter 10 value 374.392494
## iter 10 value 374.392494
## final value 374.392494
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 411.255312
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 374.446101
## iter 10 value 374.446101
## iter 10 value 374.446101
## final value 374.446101
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 352.792114
## iter 10 value 352.792111
## iter 10 value 352.792111
## final value 352.792111
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 400.589977
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 352.868524
## iter 10 value 352.868522
## iter 10 value 352.868522
## final value 352.868522
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 367.459858
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 410.336503
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 367.526043
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 361.928018
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 401.510577
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 361.987362
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 353.664516
## final value 353.664502
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 398.446936
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 353.733429
## final value 353.733415
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 376.662697
## final value 376.662654
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 413.902664
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 376.717948
## final value 376.717905
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 364.624146
## iter 10 value 364.624145
## iter 10 value 364.624145
## final value 364.624145
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 402.783558
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## iter 10 value 364.680086
## iter 10 value 364.680086
## iter 10 value 364.680086
## final value 364.680086
## converged
## # weights: 4 (3 variable)
## initial value 554.517744
## final value 401.160559
## converged
## [1] "qda"
## [1] "rf"
## note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
##
## [1] "adaboost"
names(fits) <- models
pred <- sapply(fits, function(object)
predict(object, newdata = mnist_27$test))
dim(pred)
## [1] 200 10
acc_hat <- sapply(fits, function(fit) min(fit$results$Accuracy))
mean(acc_hat)
## [1] 0.8072548
ind <- acc_hat >= mean(acc_hat)
head(ind)
## glm lda naive_bayes svmLinear knn gamLoess
## FALSE FALSE TRUE FALSE FALSE TRUE
votes <- rowMeans(pred[,ind] == "7")
head(votes)
## [1] 0 1 1 1 1 0
y_hat <- ifelse(votes>=0.5, 7, 2)
mean(y_hat == mnist_27$test$y)
## [1] 0.815