I'm trying to tune a regularized regression, but the tune grid command returns several errors. I tried several grids, several recipes, but even using only one predictor, it still returns an error.
Here's a reproducible example
df <- tibble(normalized_losses = c(NA, NA, NA, 164, 164, NA,
158, NA, 158, NA, 192, 192, 188, 188, NA, NA, NA, NA, 121, 98,
81, 118, 118, 118, 148, 148, 148, 148, 110, 145, 137, 137, 101,
101, 101, 110, 78, 106, 106, 85, 85, 85, 107, NA, NA, NA, NA,
145, NA, NA, 104, 104, 104, 113, 113, 150, 150, 150, 150, 129,
115, 129, 115, NA, 115, 118, NA, 93, 93, 93, 93, NA, 142, NA,
NA, NA, 161, 161, 161, 161, 153, 153, NA, NA, NA, 125, 125, 125,
137, 128, 128, 128, 122, 103, 128, 128, 122, 103, 168, 106, 106,
128, 108, 108, 194, 194, 231, 161, 161, NA, NA, 161, 161, NA,
NA, 161, 161, 161, 119, 119, 154, 154, 154, 74, NA, 186, NA,
NA, NA, NA, NA, NA, 150, 104, 150, 104, 150, 104, 83, 83, 83,
102, 102, 102, 102, 102, 89, 89, 85, 85, 87, 87, 74, 77, 81,
91, 91, 91, 91, 91, 91, 91, 91, 168, 168, 168, 168, 134, 134,
134, 134, 134, 134, 65, 65, 65, 65, 65, 197, 197, 90, NA, 122,
122, 94, 94, 94, 94, 94, NA, 256, NA, NA, NA, 103, 74, 103, 74,
103, 74, 95, 95, 95, 95, 95), Tipo_Cobustivel = c("gas", "gas",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "diesel", "gas",
"gas", "diesel", "diesel", "diesel", "diesel", "diesel", "gas",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"diesel", "gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"diesel", "gas", "diesel", "gas", "diesel", "gas", "diesel",
"gas", "diesel", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "diesel", "diesel",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"gas", "gas", "gas", "gas", "gas", "diesel", "gas", "gas", "gas",
"gas", "gas", "gas", "gas", "diesel", "gas", "diesel", "gas",
"gas", "diesel", "gas", "gas", "gas", "gas", "diesel", "gas",
"gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas", "gas",
"diesel", "gas"), Tipo_Motor = c("std", "std", "std", "std",
"std", "std", "std", "std", "turbo", "turbo", "std", "std", "std",
"std", "std", "std", "std", "std", "std", "std", "std", "std",
"std", "turbo", "std", "std", "std", "turbo", "std", "turbo",
"std", "std", "std", "std", "std", "std", "std", "std", "std",
"std", "std", "std", "std", "std", "std", "std", "std", "std",
"std", "std", "std", "std", "std", "std", "std", "std", "std",
"std", "std", "std", "std", "std", "std", "std", "std", "std",
"std", "turbo", "turbo", "turbo", "turbo", "std", "std", "std",
"std", "turbo", "std", "std", "std", "turbo", "turbo", "std",
"turbo", "turbo", "turbo", "std", "std", "turbo", "std", "std",
"std", "std", "std", "std", "std", "std", "std", "std", "std",
"std", "std", "std", "std", "std", "std", "turbo", "std", "std",
"turbo", "std", "turbo", "std", "turbo", "std", "turbo", "std",
"turbo", "turbo", "std", "turbo", "std", "std", "std", "std",
"turbo", "std", "std", "std", "std", "std", "std", "std", "std",
"std", "std", "std", "turbo", "turbo", "std", "std", "std", "std",
"std", "std", "std", "turbo", "std", "std", "std", "turbo", "std",
"std", "std", "std", "std", "std", "std", "std", "std", "std",
"std", "std", "std", "std", "std", "std", "std", "std", "std",
"std", "std", "std", "std", "std", "turbo", "std", "std", "std",
"std", "std", "std", "std", "std", "std", "std", "std", "std",
"turbo", "std", "std", "std", "std", "turbo", "std", "std", "std",
"std", "std", "turbo", "turbo", "std", "turbo", "std", "turbo",
"turbo"), N_de_Portas = c("two", "two", "two", "four", "four",
"two", "four", "four", "four", "two", "two", "four", "two", "four",
"four", "four", "two", "four", "two", "two", "four", "two", "two",
"two", "four", "four", "four", NA, "four", "two", "two", "two",
"two", "two", "two", "four", "four", "two", "two", "four", "four",
"four", "two", "four", "two", "four", "two", "four", "four",
"two", "two", "two", "two", "four", "four", "two", "two", "two",
"two", "two", "four", "two", "four", NA, "four", "four", "four",
"four", "four", "two", "four", "four", "two", "four", "two",
"two", "two", "two", "two", "two", "two", "two", "two", "two",
"two", "four", "four", "four", "four", "two", "two", "two", "four",
"four", "two", "two", "four", "four", "two", "four", "four",
"four", "four", "four", "two", "two", "two", "four", "four",
"four", "four", "four", "four", "four", "four", "four", "four",
"four", "two", "two", "four", "four", "four", "four", "two",
"two", "two", "two", "two", "two", "four", "two", "two", "four",
"two", "four", "two", "four", "two", "two", "two", "four", "four",
"four", "four", "four", "four", "four", "four", "four", "two",
"two", "four", "four", "four", "four", "four", "four", "four",
"four", "four", "four", "four", "two", "two", "two", "two", "two",
"two", "two", "two", "two", "two", "four", "four", "four", "four",
"four", "two", "two", "four", "four", "two", "two", "four", "four",
"four", "four", "four", "two", "two", "four", "four", "four",
"four", "four", "four", "four", "four", "four", "four", "four",
"four", "four", "four"), Tracao = c("rwd", "rwd", "rwd", "fwd",
"4wd", "fwd", "fwd", "fwd", "fwd", "4wd", "rwd", "rwd", "rwd",
"rwd", "rwd", "rwd", "rwd", "rwd", "fwd", "fwd", "fwd", "fwd",
"fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd",
"fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd",
"fwd", "fwd", "fwd", "rwd", "fwd", "fwd", "rwd", "rwd", "rwd",
"rwd", "fwd", "fwd", "fwd", "fwd", "fwd", "rwd", "rwd", "rwd",
"rwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "rwd", "rwd",
"rwd", "rwd", "rwd", "rwd", "rwd", "rwd", "rwd", "rwd", "rwd",
"fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd",
"fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd",
"fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd",
"fwd", "rwd", "rwd", "rwd", "rwd", "rwd", "rwd", "rwd", "rwd",
"rwd", "rwd", "rwd", "rwd", "rwd", "rwd", "fwd", "fwd", "fwd",
"fwd", "fwd", "fwd", "rwd", "rwd", "rwd", "rwd", "rwd", "rwd",
"fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd",
"fwd", "4wd", "fwd", "fwd", "fwd", "4wd", "4wd", "fwd", "fwd",
"4wd", "4wd", "fwd", "fwd", "fwd", "fwd", "4wd", "4wd", "fwd",
"fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "rwd", "rwd", "rwd",
"rwd", "rwd", "rwd", "rwd", "rwd", "rwd", "rwd", "fwd", "fwd",
"fwd", "fwd", "fwd", "rwd", "rwd", "rwd", "rwd", "fwd", "fwd",
"fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd", "fwd",
"fwd", "rwd", "rwd", "rwd", "rwd", "rwd", "rwd", "rwd", "rwd",
"rwd", "rwd", "rwd"), base_da_roda = c(88.6, 88.6, 94.5, 99.8,
99.4, 99.8, 105.8, 105.8, 105.8, 99.5, 101.2, 101.2, 101.2, 101.2,
103.5, 103.5, 103.5, 110, 88.4, 94.5, 94.5, 93.7, 93.7, 93.7,
93.7, 93.7, 93.7, 93.7, 103.3, 95.9, 86.6, 86.6, 93.7, 93.7,
93.7, 96.5, 96.5, 96.5, 96.5, 96.5, 96.5, 96.5, 96.5, 94.3, 94.5,
94.5, 96, 113, 113, 102, 93.1, 93.1, 93.1, 93.1, 93.1, 95.3,
95.3, 95.3, 95.3, 98.8, 98.8, 98.8, 98.8, 98.8, 98.8, 104.9,
104.9, 110, 110, 106.7, 115.6, 115.6, 96.6, 120.9, 112, 102.7,
93.7, 93.7, 93.7, 93, 96.3, 96.3, 95.9, 95.9, 95.9, 96.3, 96.3,
96.3, 96.3, 94.5, 94.5, 94.5, 94.5, 94.5, 94.5, 94.5, 94.5, 94.5,
95.1, 97.2, 97.2, 100.4, 100.4, 100.4, 91.3, 91.3, 99.2, 107.9,
107.9, 114.2, 114.2, 107.9, 107.9, 114.2, 114.2, 107.9, 107.9,
108, 93.7, 93.7, 93.7, 93.7, 93.7, 103.3, 95.9, 94.5, 89.5, 89.5,
89.5, 98.4, 96.1, 96.1, 99.1, 99.1, 99.1, 99.1, 99.1, 99.1, 93.7,
93.7, 93.3, 97.2, 97.2, 97.2, 97, 97, 97, 97, 96.9, 96.9, 95.7,
95.7, 95.7, 95.7, 95.7, 95.7, 95.7, 95.7, 95.7, 95.7, 95.7, 95.7,
95.7, 94.5, 94.5, 94.5, 94.5, 98.4, 98.4, 98.4, 98.4, 98.4, 98.4,
102.4, 102.4, 102.4, 102.4, 102.4, 102.9, 102.9, 104.5, 104.5,
97.3, 97.3, 97.3, 97.3, 97.3, 97.3, 97.3, 94.5, 94.5, 100.4,
100.4, 100.4, 104.3, 104.3, 104.3, 104.3, 104.3, 104.3, 109.1,
109.1, 109.1, 109.1, 109.1), Comprimento = c(168.8, 168.8, 171.2,
176.6, 176.6, 177.3, 192.7, 192.7, 192.7, 178.2, 176.8, 176.8,
176.8, 176.8, 189, 189, 193.8, 197, 141.1, 155.9, 158.8, 157.3,
157.3, 157.3, 157.3, 157.3, 157.3, 157.3, 174.6, 173.2, 144.6,
144.6, 150, 150, 150, 163.4, 157.1, 167.5, 167.5, 175.4, 175.4,
175.4, 169.1, 170.7, 155.9, 155.9, 172.6, 199.6, 199.6, 191.7,
159.1, 159.1, 159.1, 166.8, 166.8, 169, 169, 169, 169, 177.8,
177.8, 177.8, 177.8, 177.8, 177.8, 175, 175, 190.9, 190.9, 187.5,
202.6, 202.6, 180.3, 208.1, 199.2, 178.4, 157.3, 157.3, 157.3,
157.3, 173, 173, 173.2, 173.2, 173.2, 172.4, 172.4, 172.4, 172.4,
165.3, 165.3, 165.3, 165.3, 170.2, 165.3, 165.6, 165.3, 170.2,
162.4, 173.4, 173.4, 181.7, 184.6, 184.6, 170.7, 170.7, 178.5,
186.7, 186.7, 198.9, 198.9, 186.7, 186.7, 198.9, 198.9, 186.7,
186.7, 186.7, 157.3, 157.3, 157.3, 167.3, 167.3, 174.6, 173.2,
168.9, 168.9, 168.9, 168.9, 175.7, 181.5, 176.8, 186.6, 186.6,
186.6, 186.6, 186.6, 186.6, 156.9, 157.9, 157.3, 172, 172, 172,
172, 172, 173.5, 173.5, 173.6, 173.6, 158.7, 158.7, 158.7, 169.7,
169.7, 169.7, 166.3, 166.3, 166.3, 166.3, 166.3, 166.3, 166.3,
168.7, 168.7, 168.7, 168.7, 176.2, 176.2, 176.2, 176.2, 176.2,
176.2, 175.6, 175.6, 175.6, 175.6, 175.6, 183.5, 183.5, 187.8,
187.8, 171.7, 171.7, 171.7, 171.7, 171.7, 171.7, 171.7, 159.3,
165.7, 180.2, 180.2, 183.1, 188.8, 188.8, 188.8, 188.8, 188.8,
188.8, 188.8, 188.8, 188.8, 188.8, 188.8), Largura = c(64.1,
64.1, 65.5, 66.2, 66.4, 66.3, 71.4, 71.4, 71.4, 67.9, 64.8, 64.8,
64.8, 64.8, 66.9, 66.9, 67.9, 70.9, 60.3, 63.6, 63.6, 63.8, 63.8,
63.8, 63.8, 63.8, 63.8, 63.8, 64.6, 66.3, 63.9, 63.9, 64, 64,
64, 64, 63.9, 65.2, 65.2, 65.2, 62.5, 65.2, 66, 61.8, 63.6, 63.6,
65.2, 69.6, 69.6, 70.6, 64.2, 64.2, 64.2, 64.2, 64.2, 65.7, 65.7,
65.7, 65.7, 66.5, 66.5, 66.5, 66.5, 66.5, 66.5, 66.1, 66.1, 70.3,
70.3, 70.3, 71.7, 71.7, 70.5, 71.7, 72, 68, 64.4, 64.4, 64.4,
63.8, 65.4, 65.4, 66.3, 66.3, 66.3, 65.4, 65.4, 65.4, 65.4, 63.8,
63.8, 63.8, 63.8, 63.8, 63.8, 63.8, 63.8, 63.8, 63.8, 65.2, 65.2,
66.5, 66.5, 66.5, 67.9, 67.9, 67.9, 68.4, 68.4, 68.4, 68.4, 68.4,
68.4, 68.4, 68.4, 68.4, 68.4, 68.3, 63.8, 63.8, 63.8, 63.8, 63.8,
64.6, 66.3, 68.3, 65, 65, 65, 72.3, 66.5, 66.6, 66.5, 66.5, 66.5,
66.5, 66.5, 66.5, 63.4, 63.6, 63.8, 65.4, 65.4, 65.4, 65.4, 65.4,
65.4, 65.4, 65.4, 65.4, 63.6, 63.6, 63.6, 63.6, 63.6, 63.6, 64.4,
64.4, 64.4, 64.4, 64.4, 64.4, 64.4, 64, 64, 64, 64, 65.6, 65.6,
65.6, 65.6, 65.6, 65.6, 66.5, 66.5, 66.5, 66.5, 66.5, 67.7, 67.7,
66.5, 66.5, 65.5, 65.5, 65.5, 65.5, 65.5, 65.5, 65.5, 64.2, 64,
66.9, 66.9, 66.9, 67.2, 67.2, 67.2, 67.2, 67.2, 67.2, 68.9, 68.8,
68.9, 68.9, 68.9), Altura = c(48.8, 48.8, 52.4, 54.3, 54.3, 53.1,
55.7, 55.7, 55.9, 52, 54.3, 54.3, 54.3, 54.3, 55.7, 55.7, 53.7,
56.3, 53.2, 52, 52, 50.8, 50.8, 50.8, 50.6, 50.6, 50.6, 50.6,
59.8, 50.2, 50.8, 50.8, 52.6, 52.6, 52.6, 54.5, 58.3, 53.3, 53.3,
54.1, 54.1, 54.1, 51, 53.5, 52, 52, 51.4, 52.8, 52.8, 47.8, 54.1,
54.1, 54.1, 54.1, 54.1, 49.6, 49.6, 49.6, 49.6, 53.7, 55.5, 53.7,
55.5, 55.5, 55.5, 54.4, 54.4, 56.5, 58.7, 54.9, 56.3, 56.5, 50.8,
56.7, 55.4, 54.8, 50.8, 50.8, 50.8, 50.8, 49.4, 49.4, 50.2, 50.2,
50.2, 51.6, 51.6, 51.6, 51.6, 54.5, 54.5, 54.5, 54.5, 53.5, 54.5,
53.3, 54.5, 53.5, 53.3, 54.7, 54.7, 55.1, 56.1, 55.1, 49.7, 49.7,
49.7, 56.7, 56.7, 58.7, 58.7, 56.7, 56.7, 56.7, 58.7, 56.7, 56.7,
56, 50.8, 50.8, 50.6, 50.8, 50.8, 59.8, 50.2, 50.2, 51.6, 51.6,
51.6, 50.5, 55.2, 50.5, 56.1, 56.1, 56.1, 56.1, 56.1, 56.1, 53.7,
53.7, 55.7, 52.5, 52.5, 52.5, 54.3, 54.3, 53, 53, 54.9, 54.9,
54.5, 54.5, 54.5, 59.1, 59.1, 59.1, 53, 52.8, 53, 52.8, 53, 52.8,
52.8, 52.6, 52.6, 52.6, 52.6, 52, 52, 52, 52, 52, 53, 54.9, 54.9,
53.9, 54.9, 53.9, 52, 52, 54.1, 54.1, 55.7, 55.7, 55.7, 55.7,
55.7, 55.7, 55.7, 55.6, 51.4, 55.1, 55.1, 55.1, 56.2, 57.5, 56.2,
57.5, 56.2, 57.5, 55.5, 55.5, 55.5, 55.5, 55.5), Peso_do_Carburador = c(2548,
2548, 2823, 2337, 2824, 2507, 2844, 2954, 3086, 3053, 2395, 2395,
2710, 2765, 3055, 3230, 3380, 3505, 1488, 1874, 1909, 1876, 1876,
2128, 1967, 1989, 1989, 2191, 2535, 2811, 1713, 1819, 1837, 1940,
1956, 2010, 2024, 2236, 2289, 2304, 2372, 2465, 2293, 2337, 1874,
1909, 2734, 4066, 4066, 3950, 1890, 1900, 1905, 1945, 1950, 2380,
2380, 2385, 2500, 2385, 2410, 2385, 2410, 2443, 2425, 2670, 2700,
3515, 3750, 3495, 3770, 3740, 3685, 3900, 3715, 2910, 1918, 1944,
2004, 2145, 2370, 2328, 2833, 2921, 2926, 2365, 2405, 2403, 2403,
1889, 2017, 1918, 1938, 2024, 1951, 2028, 1971, 2037, 2008, 2324,
2302, 3095, 3296, 3060, 3071, 3139, 3139, 3020, 3197, 3230, 3430,
3075, 3252, 3285, 3485, 3075, 3252, 3130, 1918, 2128, 1967, 1989,
2191, 2535, 2818, 2778, 2756, 2756, 2800, 3366, 2579, 2460, 2658,
2695, 2707, 2758, 2808, 2847, 2050, 2120, 2240, 2145, 2190, 2340,
2385, 2510, 2290, 2455, 2420, 2650, 1985, 2040, 2015, 2280, 2290,
3110, 2081, 2109, 2275, 2275, 2094, 2122, 2140, 2169, 2204, 2265,
2300, 2540, 2536, 2551, 2679, 2714, 2975, 2326, 2480, 2414, 2414,
2458, 2976, 3016, 3131, 3151, 2261, 2209, 2264, 2212, 2275, 2319,
2300, 2254, 2221, 2661, 2579, 2563, 2912, 3034, 2935, 3042, 3045,
3157, 2952, 3049, 3012, 3217, 3062), tamanho_do_motor = c(130,
130, 152, 109, 136, 136, 136, 136, 131, 131, 108, 108, 164, 164,
164, 209, 209, 209, 61, 90, 90, 90, 90, 98, 90, 90, 90, 98, 122,
156, 92, 92, 79, 92, 92, 92, 92, 110, 110, 110, 110, 110, 110,
111, 90, 90, 119, 258, 258, 326, 91, 91, 91, 91, 91, 70, 70,
70, 80, 122, 122, 122, 122, 122, 122, 140, 134, 183, 183, 183,
183, 234, 234, 308, 304, 140, 92, 92, 92, 98, 110, 122, 156,
156, 156, 122, 122, 110, 110, 97, 103, 97, 97, 97, 97, 97, 97,
97, 97, 120, 120, 181, 181, 181, 181, 181, 181, 120, 152, 120,
152, 120, 152, 120, 152, 120, 152, 134, 90, 98, 90, 90, 98, 122,
156, 151, 194, 194, 194, 203, 132, 132, 121, 121, 121, 121, 121,
121, 97, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
92, 92, 92, 92, 92, 92, 98, 98, 110, 110, 98, 98, 98, 98, 98,
98, 98, 146, 146, 146, 146, 146, 146, 122, 110, 122, 122, 122,
171, 171, 171, 161, 97, 109, 97, 109, 109, 97, 109, 109, 109,
136, 97, 109, 141, 141, 141, 141, 130, 130, 141, 141, 173, 145,
141), Calibre_dos_pneus = c(3.47, 3.47, 2.68, 3.19, 3.19, 3.19,
3.19, 3.19, 3.13, 3.13, 3.5, 3.5, 3.31, 3.31, 3.31, 3.62, 3.62,
3.62, 2.91, 3.03, 3.03, 2.97, 2.97, 3.03, 2.97, 2.97, 2.97, 3.03,
3.34, 3.6, 2.91, 2.91, 2.91, 2.91, 2.91, 2.91, 2.92, 3.15, 3.15,
3.15, 3.15, 3.15, 3.15, 3.31, 3.03, 3.03, 3.43, 3.63, 3.63, 3.54,
3.03, 3.03, 3.03, 3.03, 3.08, NA, NA, NA, NA, 3.39, 3.39, 3.39,
3.39, 3.39, 3.39, 3.76, 3.43, 3.58, 3.58, 3.58, 3.58, 3.46, 3.46,
3.8, 3.8, 3.78, 2.97, 2.97, 2.97, 3.03, 3.17, 3.35, 3.58, 3.59,
3.59, 3.35, 3.35, 3.17, 3.17, 3.15, 2.99, 3.15, 3.15, 3.15, 3.15,
3.15, 3.15, 3.15, 3.15, 3.33, 3.33, 3.43, 3.43, 3.43, 3.43, 3.43,
3.43, 3.46, 3.7, 3.46, 3.7, 3.46, 3.7, 3.46, 3.7, 3.46, 3.7,
3.61, 2.97, 3.03, 2.97, 2.97, 2.97, 3.35, 3.59, 3.94, 3.74, 3.74,
3.74, 3.94, 3.46, 3.46, 3.54, 3.54, 2.54, 3.54, 3.54, 3.54, 3.62,
3.62, 3.62, 3.62, 3.62, 3.62, 3.62, 3.62, 3.62, 3.62, 3.62, 3.62,
3.05, 3.05, 3.05, 3.05, 3.05, 3.05, 3.19, 3.19, 3.27, 3.27, 3.19,
3.19, 3.19, 3.19, 3.19, 3.24, 3.24, 3.62, 3.62, 3.62, 3.62, 3.62,
3.62, 3.31, 3.27, 3.31, 3.31, 3.31, 3.27, 3.27, 3.27, 3.27, 3.01,
3.19, 3.01, 3.19, 3.19, 3.01, 3.19, 3.19, 3.19, 3.19, 3.01, 3.19,
3.78, 3.78, 3.78, 3.78, 3.62, 3.62, 3.78, 3.78, 3.58, 3.01, 3.78
), Derrame = c(2.68, 2.68, 3.47, 3.4, 3.4, 3.4, 3.4, 3.4, 3.4,
3.4, 2.8, 2.8, 3.19, 3.19, 3.19, 3.39, 3.39, 3.39, 3.03, 3.11,
3.11, 3.23, 3.23, 3.39, 3.23, 3.23, 3.23, 3.39, 3.46, 3.9, 3.41,
3.41, 3.07, 3.41, 3.41, 3.41, 3.41, 3.58, 3.58, 3.58, 3.58, 3.58,
3.58, 3.23, 3.11, 3.11, 3.23, 4.17, 4.17, 2.76, 3.15, 3.15, 3.15,
3.15, 3.15, NA, NA, NA, NA, 3.39, 3.39, 3.39, 3.39, 3.39, 3.39,
3.16, 3.64, 3.64, 3.64, 3.64, 3.64, 3.1, 3.1, 3.35, 3.35, 3.12,
3.23, 3.23, 3.23, 3.39, 3.46, 3.46, 3.86, 3.86, 3.86, 3.46, 3.46,
3.46, 3.46, 3.29, 3.47, 3.29, 3.29, 3.29, 3.29, 3.29, 3.29, 3.29,
3.29, 3.47, 3.47, 3.27, 3.27, 3.27, 3.27, 3.27, 3.27, 3.19, 3.52,
3.19, 3.52, 2.19, 3.52, 2.19, 3.52, 3.19, 3.52, 3.21, 3.23, 3.39,
3.23, 3.23, 3.23, 3.46, 3.86, 3.11, 2.9, 2.9, 2.9, 3.11, 3.9,
3.9, 3.07, 3.07, 2.07, 3.07, 3.07, 3.07, 2.36, 2.64, 2.64, 2.64,
2.64, 2.64, 2.64, 2.64, 2.64, 2.64, 2.64, 2.64, 3.03, 3.03, 3.03,
3.03, 3.03, 3.03, 3.03, 3.03, 3.35, 3.35, 3.03, 3.03, 3.03, 3.03,
3.03, 3.08, 3.08, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.54, 3.35, 3.54,
3.54, 3.54, 3.35, 3.35, 3.35, 3.35, 3.4, 3.4, 3.4, 3.4, 3.4,
3.4, 3.4, 3.4, 3.4, 3.4, 3.4, 3.4, 3.15, 3.15, 3.15, 3.15, 3.15,
3.15, 3.15, 3.15, 2.87, 3.4, 3.15), Taxa_de_Compressao = c(9,
9, 9, 10, 8, 8.5, 8.5, 8.5, 8.3, 7, 8.8, 8.8, 9, 9, 9, 8, 8,
8, 9.5, 9.6, 9.6, 9.41, 9.4, 7.6, 9.4, 9.4, 9.4, 7.6, 8.5, 7,
9.6, 9.2, 10.1, 9.2, 9.2, 9.2, 9.2, 9, 9, 9, 9, 9, 9.1, 8.5,
9.6, 9.6, 9.2, 8.1, 8.1, 11.5, 9, 9, 9, 9, 9, 9.4, 9.4, 9.4,
9.4, 8.6, 8.6, 8.6, 8.6, 22.7, 8.6, 8, 22, 21.5, 21.5, 21.5,
21.5, 8.3, 8.3, 8, 8, 8, 9.4, 9.4, 9.4, 7.6, 7.5, 8.5, 7, 7,
7, 8.5, 8.5, 7.5, 7.5, 9.4, 21.9, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4,
9.4, 9.4, 8.5, 8.5, 9, 9, 9, 9, 7.8, 9, 8.4, 21, 8.4, 21, 8.4,
21, 8.4, 21, 8.4, 21, 7, 9.4, 7.6, 9.4, 9.4, 9.4, 8.5, 7, 9.5,
9.5, 9.5, 9.5, 10, 8.7, 8.7, 9.31, 9.3, 9.3, 9.3, 9, 9, 9, 8.7,
8.7, 9.5, 9.5, 9, 9, 7.7, 9, 9, 9, 7.7, 9, 9, 9, 9, 9, 9, 9,
9, 22.5, 22.5, 9, 9, 9, 9, 9, 9.4, 9.4, 9.3, 9.3, 9.3, 9.3, 9.3,
9.3, 8.7, 22.5, 8.7, 8.7, 8.7, 9.3, 9.3, 9.2, 9.2, 23, 9, 23,
9, 9, 23, 10, 8.5, 8.5, 8.5, 23, 9, 9.5, 9.5, 9.5, 9.5, 7.5,
7.5, 9.5, 8.7, 8.8, 23, 9.5), Cavalos = c(111, 111, 154, 102,
115, 110, 110, 110, 140, 160, 101, 101, 121, 121, 121, 182, 182,
182, 48, 70, 70, 68, 68, 102, 68, 68, 68, 102, 88, 145, 58, 76,
60, 76, 76, 76, 76, 86, 86, 86, 86, 101, 100, 78, 70, 70, 90,
176, 176, 262, 68, 68, 68, 68, 68, 101, 101, 101, 135, 84, 84,
84, 84, 64, 84, 120, 72, 123, 123, 123, 123, 155, 155, 184, 184,
175, 68, 68, 68, 102, 116, 88, 145, 145, 145, 88, 88, 116, 116,
69, 55, 69, 69, 69, 69, 69, 69, 69, 69, 97, 97, 152, 152, 152,
160, 200, 160, 97, 95, 97, 95, 95, 95, 95, 95, 97, 95, 142, 68,
102, 68, 68, 68, 88, 145, 143, 207, 207, 207, 288, NA, NA, 110,
110, 110, 110, 160, 160, 69, 73, 73, 82, 82, 94, 82, 111, 82,
94, 82, 111, 62, 62, 62, 62, 62, 62, 70, 70, 56, 56, 70, 70,
70, 70, 70, 112, 112, 116, 116, 116, 116, 116, 116, 92, 73, 92,
92, 92, 161, 161, 156, 156, 52, 85, 52, 85, 85, 68, 100, 90,
90, 110, 68, 88, 114, 114, 114, 114, 162, 162, 114, 160, 134,
106, 114), rpm_maximo = c(5000, 5000, 5000, 5500, 5500, 5500,
5500, 5500, 5500, 5500, 5800, 5800, 4250, 4250, 4250, 5400, 5400,
5400, 5100, 5400, 5400, 5500, 5500, 5500, 5500, 5500, 5500, 5500,
5000, 5000, 4800, 6000, 5500, 6000, 6000, 6000, 6000, 5800, 5800,
5800, 5800, 5800, 5500, 4800, 5400, 5400, 5000, 4750, 4750, 5000,
5000, 5000, 5000, 5000, 5000, 6000, 6000, 6000, 6000, 4800, 4800,
4800, 4800, 4650, 4800, 5000, 4200, 4350, 4350, 4350, 4350, 4750,
4750, 4500, 4500, 5000, 5500, 5500, 5500, 5500, 5500, 5000, 5000,
5000, 5000, 5000, 5000, 5500, 5500, 5200, 4800, 5200, 5200, 5200,
5200, 5200, 5200, 5200, 5200, 5200, 5200, 5200, 5200, 5200, 5200,
5200, 5200, 5000, 4150, 5000, 4150, 5000, 4150, 5000, 4150, 5000,
4150, 5600, 5500, 5500, 5500, 5500, 5500, 5000, 5000, 5500, 5900,
5900, 5900, 5750, NA, NA, 5250, 5250, 5250, 5250, 5500, 5500,
4900, 4400, 4400, 4800, 4400, 5200, 4800, 4800, 4800, 5200, 4800,
4800, 4800, 4800, 4800, 4800, 4800, 4800, 4800, 4800, 4500, 4500,
4800, 4800, 4800, 4800, 4800, 6600, 6600, 4800, 4800, 4800, 4800,
4800, 4800, 4200, 4500, 4200, 4200, 4200, 5200, 5200, 5200, 5200,
4800, 5250, 4800, 5250, 5250, 4500, 5500, 5500, 5500, 5500, 4500,
5500, 5400, 5400, 5400, 5400, 5100, 5100, 5400, 5300, 5500, 4800,
5400), city_mpg = c(21, 21, 19, 24, 18, 19, 19, 19, 17, 16, 23,
23, 21, 21, 20, 16, 16, 15, 47, 38, 38, 37, 31, 24, 31, 31, 31,
24, 24, 19, 49, 31, 38, 30, 30, 30, 30, 27, 27, 27, 27, 24, 25,
24, 38, 38, 24, 15, 15, 13, 30, 31, 31, 31, 31, 17, 17, 17, 16,
26, 26, 26, 26, 36, 26, 19, 31, 22, 22, 22, 22, 16, 16, 14, 14,
19, 37, 31, 31, 24, 23, 25, 19, 19, 19, 25, 25, 23, 23, 31, 45,
31, 31, 31, 31, 31, 31, 31, 31, 27, 27, 17, 17, 19, 19, 17, 19,
19, 28, 19, 25, 19, 28, 19, 25, 19, 28, 18, 37, 24, 31, 31, 31,
24, 19, 19, 17, 17, 17, 17, 23, 23, 21, 21, 21, 21, 19, 19, 31,
26, 26, 32, 28, 26, 24, 24, 28, 25, 23, 23, 35, 31, 31, 31, 27,
27, 30, 30, 34, 38, 38, 28, 28, 29, 29, 26, 26, 24, 24, 24, 24,
24, 24, 29, 30, 27, 27, 27, 20, 19, 20, 19, 37, 27, 37, 27, 27,
37, 26, 24, 24, 19, 33, 25, 23, 23, 24, 24, 17, 17, 23, 19, 18,
26, 19), highway_mpg = c(27, 27, 26, 30, 22, 25, 25, 25, 20,
22, 29, 29, 28, 28, 25, 22, 22, 20, 53, 43, 43, 41, 38, 30, 38,
38, 38, 30, 30, 24, 54, 38, 42, 34, 34, 34, 34, 33, 33, 33, 33,
28, 31, 29, 43, 43, 29, 19, 19, 17, 31, 38, 38, 38, 38, 23, 23,
23, 23, 32, 32, 32, 32, 42, 32, 27, 39, 25, 25, 25, 25, 18, 18,
16, 16, 24, 41, 38, 38, 30, 30, 32, 24, 24, 24, 32, 32, 30, 30,
37, 50, 37, 37, 37, 37, 37, 37, 37, 37, 34, 34, 22, 22, 25, 25,
23, 25, 24, 33, 24, 25, 24, 33, 24, 25, 24, 33, 24, 41, 30, 38,
38, 38, 30, 24, 27, 25, 25, 25, 28, 31, 31, 28, 28, 28, 28, 26,
26, 36, 31, 31, 37, 33, 32, 25, 29, 32, 31, 29, 23, 39, 38, 38,
37, 32, 32, 37, 37, 36, 47, 47, 34, 34, 34, 34, 29, 29, 30, 30,
30, 30, 30, 30, 34, 33, 32, 32, 32, 24, 24, 24, 24, 46, 34, 46,
34, 34, 42, 32, 29, 29, 24, 38, 31, 28, 28, 28, 28, 22, 22, 28,
25, 23, 27, 25), Preco = c(13495, 16500, 16500, 13950, 17450,
15250, 17710, 18920, 23875, NA, 16430, 16925, 20970, 21105, 24565,
30760, 41315, 36880, 5151, 6295, 6575, 5572, 6377, 7957, 6229,
6692, 7609, 8558, 8921, 12964, 6479, 6855, 5399, 6529, 7129,
7295, 7295, 7895, 9095, 8845, 10295, 12945, 10345, 6785, NA,
NA, 11048, 32250, 35550, 36000, 5195, 6095, 6795, 6695, 7395,
10945, 11845, 13645, 15645, 8845, 8495, 10595, 10245, 10795,
11245, 18280, 18344, 25552, 28248, 28176, 31600, 34184, 35056,
40960, 45400, 16503, 5389, 6189, 6669, 7689, 9959, 8499, 12629,
14869, 14489, 6989, 8189, 9279, 9279, 5499, 7099, 6649, 6849,
7349, 7299, 7799, 7499, 7999, 8249, 8949, 9549, 13499, 14399,
13499, 17199, 19699, 18399, 11900, 13200, 12440, 13860, 15580,
16900, 16695, 17075, 16630, 17950, 18150, 5572, 7957, 6229, 6692,
7609, 8921, 12764, 22018, 32528, 34028, 37028, NA, 9295, 9895,
11850, 12170, 15040, 15510, 18150, 18620, 5118, 7053, 7603, 7126,
7775, 9960, 9233, 11259, 7463, 10198, 8013, 11694, 5348, 6338,
6488, 6918, 7898, 8778, 6938, 7198, 7898, 7788, 7738, 8358, 9258,
8058, 8238, 9298, 9538, 8449, 9639, 9989, 11199, 11549, 17669,
8948, 10698, 9988, 10898, 11248, 16558, 15998, 15690, 15750,
7775, 7975, 7995, 8195, 8495, 9495, 9995, 11595, 9980, 13295,
13845, 12290, 12940, 13415, 15985, 16515, 18420, 18950, 16845,
19045, 21485, 22470, 22625))
split <- initial_split(df, prop = .8, strata = Preco)
df_treino <- training(split)
df_teste <- testing(split)
######### Receita #################
receita1 <- recipe(Preco ~ Largura, data = df_treino) %>%
step_naomit()
lm_model_lasso <- linear_reg() %>%
set_engine('glmnet') %>%
set_args(penalty = tune(),
mixture = tune()
)
lm_lasso_workflow <- workflow() %>%
add_model(lm_model_lasso) %>%
add_recipe(receita1)
########## Definindo grade de busca ###############
lm_grid <- grid_latin_hypercube(penalty(),
mixture(), size = 30)
######## Tunando o modelo #################
bts <- bootstraps(df_treino,times = 5,strata = Preco)
lm_tune <-tune_grid(lm_lasso_workflow,
resamples = bts,
grid = lm_grid)
My sessionInfo:
> sessionInfo()
R version 4.2.0 (2022-04-22 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=Portuguese_Brazil.utf8 LC_CTYPE=Portuguese_Brazil.utf8
[3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C
[5] LC_TIME=Portuguese_Brazil.utf8
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] MLDataR_0.1.3 pacman_0.5.1 ranger_0.13.1
[4] glmnet_4.1-4 Matrix_1.4-1 vip_0.3.2
[7] DataExplorer_0.8.2 readxl_1.4.0 yardstick_1.0.0
[10] workflowsets_0.2.1 workflows_0.2.6 tune_0.2.0
[13] tidyr_1.2.0 tibble_3.1.7 rsample_0.1.1
[16] recipes_0.2.0 purrr_0.3.4 parsnip_0.2.1
[19] modeldata_0.1.1 infer_1.0.0 ggplot2_3.3.6
[22] dplyr_1.0.9 dials_0.1.1 scales_1.2.0
[25] broom_0.8.0 tidymodels_0.2.0
loaded via a namespace (and not attached):
[1] splines_4.2.0 foreach_1.5.2 warp_0.2.0
[4] prodlim_2019.11.13 BiocManager_1.30.18 GPfit_1.0-8
[7] cellranger_1.1.0 globals_0.15.0 ipred_0.9-13
[10] pillar_1.7.0 backports_1.4.1 lattice_0.20-45
[13] glue_1.6.2 digest_0.6.29 hardhat_1.0.0
[16] colorspace_2.0-3 htmltools_0.5.2 timeDate_3043.102
[19] pkgconfig_2.0.3 lhs_1.1.5 DiceDesign_1.9
[22] listenv_0.8.0 slider_0.2.2 gower_1.0.0
[25] lava_1.6.10 farver_2.1.0 generics_0.1.2
[28] ellipsis_0.3.2 withr_2.5.0 furrr_0.3.0
[31] nnet_7.3-17 cli_3.3.0 survival_3.3-1
[34] magrittr_2.0.3 crayon_1.5.1 evaluate_0.15
[37] future_1.26.1 fansi_1.0.3 parallelly_1.31.1
[40] MASS_7.3-56 class_7.3-20 tools_4.2.0
[43] data.table_1.14.2 lifecycle_1.0.1 munsell_0.5.0
[46] networkD3_0.4 compiler_4.2.0 rlang_1.0.2
[49] grid_4.2.0 iterators_1.0.14 rstudioapi_0.13
[52] htmlwidgets_1.5.4 igraph_1.3.1 labeling_0.4.2
[55] rmarkdown_2.14 gtable_0.3.0 codetools_0.2-18
[58] R6_2.5.1 gridExtra_2.3 lubridate_1.8.0
[61] knitr_1.39 fastmap_1.1.0 future.apply_1.9.0
[64] utf8_1.2.2 shape_1.4.6 Rcpp_1.0.8.3
[67] vctrs_0.4.1 rpart_4.1.16 tidyselect_1.1.2
[70] xfun_0.31
Can anyone help me with this issue? After a lot of trying around here, I gave up.
You are running into a couple of issues right here. First The outcome has some missing data which will not work when we are modeling here. I would suggest filtering out those observations before you do the test training split
split <- df %>%
filter(!is.na(Preco)) %>%
initial_split(prop = .8, strata = Preco)
Secondly It looked like you tried to make a minimal recipe. It became a little too minimal as glmnet wants at least 2 predictors, if you make sure you have more columns it should work.
I made a fairly minimal recipe that handles missing data in predictors and turning character values into dummy variables (because glmnet only accepts numeric predictors)
receita1 <- recipe(Preco ~ ., data = df_treino) %>%
step_impute_mean(all_numeric_predictors()) %>%
step_unknown(all_nominal_predictors()) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors())
With these two changes, it should run
library(tidymodels)
df <- tibble() # Code not included for clarify
split <- df %>%
filter(!is.na(Preco)) %>%
initial_split(prop = .8, strata = Preco)
df_treino <- training(split)
df_teste <- testing(split)
######### Receita #################
receita1 <- recipe(Preco ~ ., data = df_treino) %>%
step_impute_mean(all_numeric_predictors()) %>%
step_unknown(all_nominal_predictors()) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors())
lm_model_lasso <- linear_reg() %>%
set_engine('glmnet') %>%
set_args(penalty = tune(),
mixture = tune())
lm_lasso_workflow <- workflow() %>%
add_model(lm_model_lasso) %>%
add_recipe(receita1)
########## Definindo grade de busca ###############
lm_grid <- grid_latin_hypercube(penalty(),
mixture(), size = 30)
######## Tunando o modelo #################
bts <- bootstraps(df_treino,times = 5, strata = Preco)
lm_tune <- tune_grid(lm_lasso_workflow,
resamples = bts,
grid = lm_grid)
lm_tune
#> # Tuning results
#> # Bootstrap sampling using stratification
#> # A tibble: 5 × 4
#> splits id .metrics .notes
#> <list> <chr> <list> <list>
#> 1 <split [160/57]> Bootstrap1 <tibble [60 × 6]> <tibble [0 × 3]>
#> 2 <split [160/59]> Bootstrap2 <tibble [60 × 6]> <tibble [0 × 3]>
#> 3 <split [160/55]> Bootstrap3 <tibble [60 × 6]> <tibble [0 × 3]>
#> 4 <split [160/65]> Bootstrap4 <tibble [60 × 6]> <tibble [0 × 3]>
#> 5 <split [160/61]> Bootstrap5 <tibble [60 × 6]> <tibble [0 × 3]>
Created on 2022-06-08 by the reprex package (v2.0.1)