MARS model using library earth in R: Why the both “cv_mars” and “mars1” are producing the same results?

[surajenv]

I used

hyper_grid <- expand.grid(
      degree = 1:10, 
      nprune = seq(2, 1000, length.out = 100) %>% floor()
    )
head(hyper_grid)

for tuning the MARS model

set.seed(123)  # for reproducibility
    cv_mars <- train(
      x = subset(train, select = -Rm),
      y = train[,1],
      method = "earth",
      metric = "RMSE",
      trControl = trainControl(method = "cv", number = 10),
      tuneGrid = hyper_grid)

used to train the MARS model with earth library in R and then

# View RMSE R2 MAE #
    cv_mars$results %>%
      filter(nprune == cv_mars$bestTune$nprune, degree == cv_mars$bestTune$degree)

used to evaluate the performance matrices. HOWEVER, I am looking for

1. Why both models ("cv_mars" and "mars1") produced the same results? What is missing here? Even used another hyper_grid as

`hyper_grid <- expand.grid(
  degree = 1:3, 
  nprune = seq(2, 100, length.out = 10) %>% floor()
)
head(hyper_grid)`

the data is as:

> dput(train)
structure(list(Rm = c(0, 86.7072111207646, 86.2148856067188, 
85.0275123081378, 98.9574283231972, 99.9710396756444, 99.8841587025775, 
99.7393570807993, 99.9710396756444, 55.4133333333333, 54.5066666666667, 
56.8533333333333, 99.68, 99.8133333333333, 99.92, 99.8933333333333, 
99.8666666666667, 99.8666666666667, 99.9466666666667, 99.8666666666667, 
99.84, 99.5675675675676, 99.2972972972973, 99.4324324324324, 
99.5675675675676, 99.5135135135135, 99.4864864864865, 99.5405405405405, 
99.8108108108108, 99.7837837837838, 99.8648648648649, 99.8648648648649, 
99.8648648648649, 99.7837837837838, 99.8918918918919, 99.8378378378378, 
99.2432432432432, 96.3243243243243, 96.2432432432432, 97.1891891891892, 
99.8477157360406, 99.7457627118644, 99.9717514124294, 99.8870056497175, 
99.7933884297521, 99.2492492492492, 99.6996996996997, 99.7597597597598, 
98.2548701298701, 96.25, 96.6764705882353, 96.889705882353, 79.9844236760125, 
51.2048192771084, 51.3052208835341, 38.4538152610442, 40.9037558685446, 
40.5587668593449, 30.3468208092486, 28.9980732177264, 30.7563025210084, 
26.551724137931, 24.7126436781609, 28.5057471264368), IC = c(50, 
50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 
50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 
50, 50, 50, 50, 50, 50, 50, 25, 50, 50, 50, 75, 100, 100, 100, 
150, 200, 200, 200, 250, 500, 500, 500, 1000, 1250, 1250, 1250, 
1500, 2000, 2000, 2000), Dose = c(0, 2, 2, 2, 5, 10, 10, 10, 
15, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10), CT = c(120, 120, 120, 120, 120, 
120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 
120, 120, 120, 5, 5, 5, 15, 30, 30, 30, 60, 90, 90, 90, 120, 
120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 
120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 
120, 120, 120, 120, 120), pH = c(4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 
2, 2, 4, 6, 6, 6, 8, 10, 10, 10, 12, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4), Na = c(0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0.01, 0.01, 0.1, 0.5, 0.5, 0.5, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0)), row.names = c(NA, -64L), class = c("tbl_df", "tbl", 
"data.frame"))

> dput(test)
structure(list(Rm = c(0, 0, 99.5945554590211, 99.1891109180423, 
99.9420793512887, 99.98, 99.76, 99.7066666666667, 99.8933333333333, 
99.9733333333333, 99.68, 99.5733333333334, 99.6216216216216, 
99.6216216216216, 99.8378378378378, 99.7567567567567, 99.8918918918919, 
99.8648648648649, 99.1351351351351, 99.1621621621622, 99.5431472081218, 
99.6954314720812, 99.5867768595041, 99.8140495867768, 97.4512987012987, 
98.2467532467532, 78.3878504672897, 79.9065420560748, 41.0211267605634, 
41.1971830985916, 19.4117647058824, 30.1680672268908), IC = c(50, 
50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 
50, 50, 50, 25, 25, 75, 75, 150, 150, 250, 250, 1000, 1000, 1500, 
1500), Dose = c(0, 0, 5, 5, 15, 15, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10), CT = c(120, 120, 120, 120, 120, 120, 120, 120, 120, 
120, 120, 120, 15, 15, 60, 60, 120, 120, 120, 120, 120, 120, 
120, 120, 120, 120, 120, 120, 120, 120, 120, 120), pH = c(4, 
4, 4, 4, 4, 4, 4, 4, 8, 8, 12, 12, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4), Na = c(0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0.1, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0)), row.names = c(NA, -32L), class = c("tbl_df", 
"tbl", "data.frame"))

hope to get your valuable suggestion and answer best regards Suraj