Я сталкиваюсь с ошибкой, когда пытаюсь уместить свою модель SVM в столбец, заполненный одинаковыми значениями (единственные столбцы).Некоторые из моих столбцов также содержат значения с небольшим отклонением.
Есть ли способ манипулировать данными для продолжения подгонки модели?
mydata = structure(list(Month = structure(c(17167, 17198, 17226, 17257,
17287, 17318), class = "Date"), Direct_Indirect_Labour = c(42849.46,
52383.57, 45978.29, 36593.28, 46295.52, 38026.65), Temporary = c(0.331351999635808,
0.440950856148265, -6760.03, 5880, 4666.4, 2780), Depreciation = c(7911,
13533, 16913, 13529, 13533, 16914), Supplies = c(6196.96, 8231.02,
7976.9, 0.383607612689957, 1290.13, 2505.16), Salary = c(79640.08,
86911.23, 109318.4, 85490.85, 94242, 86755.67), Employee_Expense = c(166.67,
166.66, 166.67, 166.67, 166.66, 166.67), Training = c(14, 14,
14, 14, 14, 1325.15), Travel_and_Internal.Meetings = c(0.756230966444127,
0.678670324850827, 0.882264748564921, 0.878525752550922, 0.583686126419343,
0.169935691752471), Outside_Services = c(43401.37, 23281.97,
7487.77, 0.65534929279238, 11667.54, 5833.77), Utilities = c(0.562701693200506,
0.559285866911523, 0.999009727593511, 0.348044516844675, 0.387320584990084,
0.633795398520306), Repairs_and_Maintenance = c(9688.21, 10769.4,
39249.01, 26952.48, 14230.63, 21605.21), Production_Hours = c(849,
989, 1099, 702, 719, 1442), Production_Volume = c(4311, 7960,
11140, 7712, 6843, 9290), Head_Count_Salary_Actual = c(7, 7,
7, 7, 7, 7), Head_Count_Wages_Actual = c(6.3, 6.2, 6.2, 6.2,
6.2, 6.2), Head_Count_Temporary_Actual = c(0.611488743126392,
0.378667761944234, 0.495445171184838, 0.483697441453114, 0.984015051205642,
0.759395990055054), Average_Salary_Actual = c(11377.2, 12415.9,
15616.9, 12213, 13463.1, 12393.7), Average_Wages_Actual = c(6801.5,
8449, 7415.9, 5902.1, 7467, 6133.3), Average_Temp_Actual = c(0.319232558691874,
0.521609056880698, 0.258009373117238, 0.355883453111164, 0.505098798498511,
0.923097091168165), Average_Salary_Pred = c(0.550580919114873,
0.811911257286556, 0.668805605382659, 0.528498480049893, 0.218548567825928,
0.963722872454673), Average_Wages_Pred = c(0.940045257052407,
0.193145794980228, 0.921505043585785, 0.574547953042202, 0.404802304483019,
0.892557216878049), Average_Temporary_Pred = c(0.189988148910925,
0.238607874466106, 0.141111821238883, 0.602298513520509, 0.340502622444183,
0.444130616402253), Average_Supply_Actual = c(1.437476224, 1.034047739,
0.716059246, 0.460293355840258, 0.188532807, 0.269662002), Average_Supply_Pred = c(0.675778884044848,
0.450004956312478, 0.90388071523048, 0.18158066291362, 0.347185597242788,
0.372686233418062), Month_Factor = structure(c(5L, 4L, 8L, 1L,
9L, 7L), .Label = c("April", "August", "December", "February",
"January", "July", "June", "March", "May", "November", "October",
"September"), class = "factor"), Quarter_Factor = structure(c(1L,
1L, 1L, 2L, 2L, 2L), .Label = c("Q1", "Q2", "Q3", "Q4"), class = "factor"),
Hours_13 = c(791, 800, 864, 977, 1011, 659), Volume_13 = c(7653,
9149, 4927, 7923, 10140, 9057), Hours_14 = c(791, 791, 800,
864, 977, 1011), Volume_14 = c(7653, 7653, 9149, 4927, 7923,
10140), Hours_22 = c(549, 636, 636, 636, 793, 793), Volume_22 = c(8280,
6179, 6179, 6179, 8972, 8972), Hours_21 = c(636, 636, 636,
793, 793, 793), Volume_21 = c(6179, 6179, 6179, 8972, 8972,
8972), Hours_15 = c(791, 791, 791, 800, 864, 977), Volume_15 = c(7653,
7653, 7653, 9149, 4927, 7923), Hours_16 = c(793, 791, 791,
791, 800, 864), Volume_16 = c(8972, 7653, 7653, 7653, 9149,
4927), Hours_17 = c(793, 793, 791, 791, 791, 800), Volume_17 = c(8972,
8972, 7653, 7653, 7653, 9149), Hours_18 = c(793, 793, 793,
791, 791, 791), Volume_18 = c(8972, 8972, 8972, 7653, 7653,
7653), Hours_19 = c(636, 793, 793, 793, 791, 791), Volume_19 = c(6179,
8972, 8972, 8972, 7653, 7653), Hours_20 = c(636, 636, 793,
793, 793, 791), Volume_20 = c(6179, 6179, 8972, 8972, 8972,
7653), Hours_23 = c(549, 549, 636, 636, 636, 793), Volume_23 = c(8280,
8280, 6179, 6179, 6179, 8972), Hours_12 = c(800, 864, 977,
1011, 659, 945), Volume_12 = c(9149, 4927, 7923, 10140, 9057,
8676), Labour_23 = c(59886.15, 74857.69, 62534.46, 62534.46,
78168.08, 64905.85), Temp_23 = c(0.279695877688937, 0.749911568011157,
3360, 3360, 4200, 0.12896179179661), Labour_12 = c(69941,
79990, 77132, 77009, 78448, 73166), Temp_12 = c(0.977637041476555,
0.789679699344561, 0.401369009260088, 0.771174287330359,
0.671730771614239, 0.981103926547803), Labour_13 = c(85652.69,
69941, 79990, 77132, 77009, 78448), Temp_13 = c(0.987518894812092,
0.977637041476555, 0.789679699344561, 0.401369009260088,
0.771174287330359, 0.671730771614239), Labour_14 = c(68522.15,
85652.69, 69941, 79990, 77132, 77009), Temp_14 = c(0.26887227059342,
0.987518894812092, 0.977637041476555, 0.789679699344561,
0.401369009260088, 0.771174287330359), Labour_15 = c(68522.15,
68522.15, 85652.69, 69941, 79990, 77132), Temp_15 = c(0.268415309977718,
0.26887227059342, 0.987518894812092, 0.977637041476555, 0.789679699344561,
0.401369009260088), Labour_16 = c(81132.31, 68522.15, 68522.15,
85652.69, 69941, 79990), Temp_16 = c(0.451536390581168, 0.268415309977718,
0.26887227059342, 0.987518894812092, 0.977637041476555, 0.789679699344561
), Labour_17 = c(64905.85, 81132.31, 68522.15, 68522.15,
85652.69, 69941), Temp_17 = c(0.437975334376097, 0.451536390581168,
0.268415309977718, 0.26887227059342, 0.987518894812092, 0.977637041476555
), Labour_18 = c(64905.85, 64905.85, 81132.31, 68522.15,
68522.15, 85652.69), Temp_18 = c(0.12896179179661, 0.437975334376097,
0.451536390581168, 0.268415309977718, 0.26887227059342, 0.987518894812092
), Labour_19 = c(78168.08, 64905.85, 64905.85, 81132.31,
68522.15, 68522.15), Temp_19 = c(4200, 0.12896179179661,
0.437975334376097, 0.451536390581168, 0.268415309977718,
0.26887227059342), Labour_20 = c(62534.46, 78168.08, 64905.85,
64905.85, 81132.31, 68522.15), Temp_20 = c(3360, 4200, 0.12896179179661,
0.437975334376097, 0.451536390581168, 0.268415309977718),
Labour_21 = c(62534.46, 62534.46, 78168.08, 64905.85, 64905.85,
81132.31), Temp_21 = c(3360, 3360, 4200, 0.12896179179661,
0.437975334376097, 0.451536390581168), Labour_22 = c(74857.69,
62534.46, 62534.46, 78168.08, 64905.85, 64905.85), Temp_22 = c(0.749911568011157,
3360, 3360, 4200, 0.12896179179661, 0.437975334376097), Depre_23 = c(29154.15,
36442.69, 27049.54, 27049.54, 33811.92, 26892.92), Supplies_23 = c(6924.92,
8656.15, 6427.38, 6427.38, 8034.23, 9220.62), Depre_12 = c(14334,
2962, 10354, 8281, 8285, 10353), Supplies_12 = c(11635, 2693,
8585, 8706, 13309, 4408), Depre_13 = c(37574.62, 14334, 2962,
10354, 8281, 8285), Supplies_13 = c(20976.54, 11635, 2693,
8585, 8706, 13309), Depre_14 = c(30059.69, 37574.62, 14334,
2962, 10354, 8281), Supplies_14 = c(16781.23, 20976.54, 11635,
2693, 8585, 8706), Depre_15 = c(30059.69, 30059.69, 37574.62,
14334, 2962, 10354), Supplies_15 = c(16781.23, 16781.23,
20976.54, 11635, 2693, 8585), Depre_16 = c(33616.15, 30059.69,
30059.69, 37574.62, 14334, 2962), Supplies_16 = c(11525.77,
16781.23, 16781.23, 20976.54, 11635, 2693), Depre_17 = c(26892.92,
33616.15, 30059.69, 30059.69, 37574.62, 14334), Supplies_17 = c(9220.62,
11525.77, 16781.23, 16781.23, 20976.54, 11635), Depre_18 = c(26892.92,
26892.92, 33616.15, 30059.69, 30059.69, 37574.62), Supplies_18 = c(9220.62,
9220.62, 11525.77, 16781.23, 16781.23, 20976.54), Depre_19 = c(33811.92,
26892.92, 26892.92, 33616.15, 30059.69, 30059.69), Supplies_19 = c(8034.23,
9220.62, 9220.62, 11525.77, 16781.23, 16781.23), Depre_20 = c(27049.54,
33811.92, 26892.92, 26892.92, 33616.15, 30059.69), Supplies_20 = c(6427.38,
8034.23, 9220.62, 9220.62, 11525.77, 16781.23), Depre_21 = c(27049.54,
27049.54, 33811.92, 26892.92, 26892.92, 33616.15), Supplies_21 = c(6427.38,
6427.38, 8034.23, 9220.62, 9220.62, 11525.77), Depre_22 = c(36442.69,
27049.54, 27049.54, 33811.92, 26892.92, 26892.92), Supplies_22 = c(8656.15,
6427.38, 6427.38, 8034.23, 9220.62, 9220.62), Sal_23 = c(52886,
52886, 49168, 49168, 49168, 49491.33), Utilities_23 = c(0.639005555375479,
0.40511339630466, 0.776422380679287, 0.782608798332512, 0.443903855746612,
256.33), Sal_12 = c(51737, 70639, 70644, 64158, 64370, 68125
), Utilities_12 = c(0.551200766931288, 0.681138247158378,
0.135812793998048, 0.521061763237231, 0.110077138687484,
0.323213608143851), Sal_13 = c(64398.67, 51737, 70639, 70644,
64158, 64370), Utilities_13 = c(0.938679515616968, 0.551200766931288,
0.681138247158378, 0.135812793998048, 0.521061763237231,
0.110077138687484), Sal_14 = c(64398.67, 64398.67, 51737,
70639, 70644, 64158), Utilities_14 = c(0.386798183922656,
0.938679515616968, 0.551200766931288, 0.681138247158378,
0.135812793998048, 0.521061763237231), Sal_15 = c(64398.67,
64398.67, 64398.67, 51737, 70639, 70644), Utilities_15 = c(0.398033457505517,
0.386798183922656, 0.938679515616968, 0.551200766931288,
0.681138247158378, 0.135812793998048), Sal_16 = c(49491.33,
64398.67, 64398.67, 64398.67, 51737, 70639), Utilities_16 = c(256.33,
0.398033457505517, 0.386798183922656, 0.938679515616968,
0.551200766931288, 0.681138247158378), Sal_17 = c(49491.33,
49491.33, 64398.67, 64398.67, 64398.67, 51737), Utilities_17 = c(256.33,
256.33, 0.398033457505517, 0.386798183922656, 0.938679515616968,
0.551200766931288), Sal_18 = c(49491.33, 49491.33, 49491.33,
64398.67, 64398.67, 64398.67), Utilities_18 = c(256.33, 256.33,
256.33, 0.398033457505517, 0.386798183922656, 0.938679515616968
), Sal_19 = c(49168, 49491.33, 49491.33, 49491.33, 64398.67,
64398.67), Utilities_19 = c(0.443903855746612, 256.33, 256.33,
256.33, 0.398033457505517, 0.386798183922656), Sal_20 = c(49168,
49168, 49491.33, 49491.33, 49491.33, 64398.67), Utilities_20 = c(0.782608798332512,
0.443903855746612, 256.33, 256.33, 256.33, 0.398033457505517
), Sal_21 = c(49168, 49168, 49168, 49491.33, 49491.33, 49491.33
), Utilities_21 = c(0.776422380679287, 0.782608798332512,
0.443903855746612, 256.33, 256.33, 256.33), Sal_22 = c(52886,
49168, 49168, 49168, 49491.33, 49491.33), Utilities_22 = c(0.40511339630466,
0.776422380679287, 0.782608798332512, 0.443903855746612,
256.33, 256.33), Rep_23 = c(11560, 11560, 27768, 27768, 27768,
17917.33), Rep_22 = c(11560, 27768, 27768, 27768, 17917.33,
17917.33), Rep_12 = c(1817, 4596, 807, 82415, 921, 34897),
Rep_13 = c(14858.67, 1817, 4596, 807, 82415, 921), Rep_14 = c(14858.67,
14858.67, 1817, 4596, 807, 82415), Rep_15 = c(14858.67, 14858.67,
14858.67, 1817, 4596, 807), Rep_16 = c(17917.33, 14858.67,
14858.67, 14858.67, 1817, 4596), Rep_17 = c(17917.33, 17917.33,
14858.67, 14858.67, 14858.67, 1817), Rep_18 = c(17917.33,
17917.33, 17917.33, 14858.67, 14858.67, 14858.67), Rep_19 = c(27768,
17917.33, 17917.33, 17917.33, 14858.67, 14858.67), Rep_20 = c(27768,
27768, 17917.33, 17917.33, 17917.33, 14858.67), Rep_21 = c(27768,
27768, 27768, 17917.33, 17917.33, 17917.33), Expense_23 = c(0.863451149477623,
0.368537076935172, 477.33, 477.33, 477.33, 0.283058275002986
), Expense_22 = c(0.368537076935172, 477.33, 477.33, 477.33,
0.283058275002986, 0.149534041364677), Expense_12 = c(0.338978823460639,
0.649076197878458, 0.993144115968607, 0.727383353351615,
0.426240362320095, 0.697347183246166), Expense_13 = c(158.67,
0.338978823460639, 0.649076197878458, 0.993144115968607,
0.727383353351615, 0.426240362320095), Expense_14 = c(158.67,
158.67, 0.338978823460639, 0.649076197878458, 0.993144115968607,
0.727383353351615), Expense_15 = c(158.67, 158.67, 158.67,
0.338978823460639, 0.649076197878458, 0.993144115968607),
Expense_16 = c(0.30971689959988, 158.67, 158.67, 158.67,
0.338978823460639, 0.649076197878458), Expense_17 = c(0.149534041364677,
0.30971689959988, 158.67, 158.67, 158.67, 0.338978823460639
), Expense_18 = c(0.283058275002986, 0.149534041364677, 0.30971689959988,
158.67, 158.67, 158.67), Expense_19 = c(477.33, 0.283058275002986,
0.149534041364677, 0.30971689959988, 158.67, 158.67), Expense_20 = c(477.33,
477.33, 0.283058275002986, 0.149534041364677, 0.30971689959988,
158.67), Expense_21 = c(477.33, 477.33, 477.33, 0.283058275002986,
0.149534041364677, 0.30971689959988), Training_23 = c(1446.33,
1446.33, 2705.33, 2705.33, 2705.33, 1981.33), Training_22 = c(1446.33,
2705.33, 2705.33, 2705.33, 1981.33, 1981.33), Training_12 = c(15,
215, 15, 15, 4415, 15), Training_13 = c(2286, 15, 215, 15,
15, 4415), Training_14 = c(2286, 2286, 15, 215, 15, 15),
Training_15 = c(2286, 2286, 2286, 15, 215, 15), Training_16 = c(1981.33,
2286, 2286, 2286, 15, 215), Training_17 = c(1981.33, 1981.33,
2286, 2286, 2286, 15), Training_18 = c(1981.33, 1981.33,
1981.33, 2286, 2286, 2286), Training_19 = c(2705.33, 1981.33,
1981.33, 1981.33, 2286, 2286), Training_20 = c(2705.33, 2705.33,
1981.33, 1981.33, 1981.33, 2286), Training_21 = c(2705.33,
2705.33, 2705.33, 1981.33, 1981.33, 1981.33), Travel_23 = c(20.33,
20.33, 881, 881, 881, 405.67), Travel_22 = c(20.33, 881,
881, 881, 405.67, 405.67), Travel_12 = c(41, 0.219461965607479,
0.419916320731863, 0.97138471880462, 0.244277428556234, 0.412949960702099
), Travel_13 = c(118.67, 41, 0.219461965607479, 0.419916320731863,
0.97138471880462, 0.244277428556234), Travel_14 = c(118.67,
118.67, 41, 0.219461965607479, 0.419916320731863, 0.97138471880462
), Travel_15 = c(118.67, 118.67, 118.67, 41, 0.219461965607479,
0.419916320731863), Travel_16 = c(405.67, 118.67, 118.67,
118.67, 41, 0.219461965607479), Travel_17 = c(405.67, 405.67,
118.67, 118.67, 118.67, 41), Travel_18 = c(405.67, 405.67,
405.67, 118.67, 118.67, 118.67), Travel_19 = c(881, 405.67,
405.67, 405.67, 118.67, 118.67), Travel_20 = c(881, 881,
405.67, 405.67, 405.67, 118.67), Travel_21 = c(881, 881,
881, 405.67, 405.67, 405.67), Outside_23 = c(6635.33, 6635.33,
7846.33, 7846.33, 7846.33, 10240.33), Outside_22 = c(6635.33,
7846.33, 7846.33, 7846.33, 10240.33, 10240.33), Outside_12 = c(8940,
6328, 21236, 11194, 16298, 22194), Outside_13 = c(7644.33,
8940, 6328, 21236, 11194, 16298), Outside_14 = c(7644.33,
7644.33, 8940, 6328, 21236, 11194), Outside_15 = c(7644.33,
7644.33, 7644.33, 8940, 6328, 21236), Outside_16 = c(10240.33,
7644.33, 7644.33, 7644.33, 8940, 6328), Outside_17 = c(10240.33,
10240.33, 7644.33, 7644.33, 7644.33, 8940), Outside_18 = c(10240.33,
10240.33, 10240.33, 7644.33, 7644.33, 7644.33), Outside_19 = c(7846.33,
10240.33, 10240.33, 10240.33, 7644.33, 7644.33), Outside_20 = c(7846.33,
7846.33, 10240.33, 10240.33, 10240.33, 7644.33), Outside_21 = c(7846.33,
7846.33, 7846.33, 10240.33, 10240.33, 10240.33), Hours_24 = c(549,
549, 549, 636, 636, 636), Volume_24 = c(8280, 8280, 8280,
6179, 6179, 6179), Labour_24 = c(59886.15, 59886.15, 74857.69,
62534.46, 62534.46, 78168.08), Temp_24 = c(0.798736015893519,
0.279695877688937, 0.749911568011157, 3360, 3360, 4200),
Depre_24 = c(29154.15, 29154.15, 36442.69, 27049.54, 27049.54,
33811.92), Supplies_24 = c(6924.92, 6924.92, 8656.15, 6427.38,
6427.38, 8034.23), Sal_24 = c(52886, 52886, 52886, 49168,
49168, 49168), Utilities_24 = c(0.476430512452498, 0.639005555375479,
0.40511339630466, 0.776422380679287, 0.782608798332512, 0.443903855746612
), Rep_24 = c(11560, 11560, 11560, 27768, 27768, 27768),
Expense_24 = c(0.866271002334543, 0.863451149477623, 0.368537076935172,
477.33, 477.33, 477.33), Training_24 = c(1446.33, 1446.33,
1446.33, 2705.33, 2705.33, 2705.33), Travel_24 = c(20.33,
20.33, 20.33, 881, 881, 881), Outside_24 = c(6635.33, 6635.33,
6635.33, 7846.33, 7846.33, 7846.33), AveWage_23 = c(5988.6,
7485.8, 6253.4, 6253.4, 7816.8, 7211.8), AveWage_22 = c(7485.8,
6253.4, 6253.4, 7816.8, 7211.8, 7211.8), AveWage_12 = c(7771.2,
8887.8, 8570.2, 12834.8, 13074.6, 12194.4), AveWage_13 = c(9517,
7771.2, 8887.8, 8570.2, 12834.8, 13074.6), AveWage_14 = c(7613.6,
9517, 7771.2, 8887.8, 8570.2, 12834.8), AveWage_15 = c(7613.6,
7613.6, 9517, 7771.2, 8887.8, 8570.2), AveWage_16 = c(9014.7,
7613.6, 7613.6, 9517, 7771.2, 8887.8), AveWage_17 = c(7211.8,
9014.7, 7613.6, 7613.6, 9517, 7771.2), AveWage_18 = c(7211.8,
7211.8, 9014.7, 7613.6, 7613.6, 9517), AveWage_19 = c(7816.8,
7211.8, 7211.8, 9014.7, 7613.6, 7613.6), AveWage_20 = c(6253.4,
7816.8, 7211.8, 7211.8, 9014.7, 7613.6), AveWage_21 = c(6253.4,
6253.4, 7816.8, 7211.8, 7211.8, 9014.7), AveWage_24 = c(5988.6,
5988.6, 7485.8, 6253.4, 6253.4, 7816.8), AveTemp_23 = c(0.512824865733273,
0.960840224893764, 0.865949163446203, 0.615993993659504,
0.981397898541763, 0.431526668346487), AveTemp_22 = c(0.960840224893764,
0.865949163446203, 0.615993993659504, 0.981397898541763,
0.431526668346487, 0.631801532348618), AveTemp_12 = c(0.120386255974881,
0.7722184719285, 0.144036578852683, 0.29368625539355, 0.717778898891993,
0.712389939487912), AveTemp_13 = c(0.822862699511461, 0.120386255974881,
0.7722184719285, 0.144036578852683, 0.29368625539355, 0.717778898891993
), AveTemp_14 = c(0.816171750589274, 0.822862699511461, 0.120386255974881,
0.7722184719285, 0.144036578852683, 0.29368625539355), AveTemp_15 = c(0.923277984373271,
0.816171750589274, 0.822862699511461, 0.120386255974881,
0.7722184719285, 0.144036578852683), AveTemp_16 = c(0.198389250622131,
0.923277984373271, 0.816171750589274, 0.822862699511461,
0.120386255974881, 0.7722184719285), AveTemp_17 = c(0.631801532348618,
0.198389250622131, 0.923277984373271, 0.816171750589274,
0.822862699511461, 0.120386255974881), AveTemp_18 = c(0.431526668346487,
0.631801532348618, 0.198389250622131, 0.923277984373271,
0.816171750589274, 0.822862699511461), AveTemp_19 = c(0.981397898541763,
0.431526668346487, 0.631801532348618, 0.198389250622131,
0.923277984373271, 0.816171750589274), AveTemp_20 = c(0.615993993659504,
0.981397898541763, 0.431526668346487, 0.631801532348618,
0.198389250622131, 0.923277984373271), AveTemp_21 = c(0.865949163446203,
0.615993993659504, 0.981397898541763, 0.431526668346487,
0.631801532348618, 0.198389250622131), AveTemp_24 = c(0.958844881202094,
0.512824865733273, 0.960840224893764, 0.865949163446203,
0.615993993659504, 0.981397898541763)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -6L), na.action = structure(c(`1` = 1L,
`2` = 2L, `3` = 3L, `4` = 4L, `5` = 5L, `6` = 6L, `7` = 7L, `8` = 8L,
`9` = 9L, `10` = 10L, `11` = 11L, `12` = 12L, `13` = 13L, `14` = 14L,
`15` = 15L, `16` = 16L, `17` = 17L, `18` = 18L, `19` = 19L, `20` = 20L,
`21` = 21L, `22` = 22L, `23` = 23L, `24` = 24L, `37` = 37L, `38` = 38L,
`39` = 39L, `40` = 40L, `41` = 41L, `42` = 42L, `43` = 43L, `44` = 44L,
`45` = 45L, `46` = 46L, `47` = 47L, `48` = 48L, `49` = 49L, `50` = 50L,
`51` = 51L, `52` = 52L, `53` = 53L, `54` = 54L, `55` = 55L, `56` = 56L,
`57` = 57L, `58` = 58L, `59` = 59L, `60` = 60L, `73` = 73L, `74` = 74L,
`75` = 75L, `76` = 76L, `77` = 77L, `78` = 78L, `79` = 79L, `80` = 80L,
`81` = 81L, `82` = 82L, `83` = 83L, `84` = 84L, `85` = 85L, `86` = 86L,
`87` = 87L, `88` = 88L, `89` = 89L, `90` = 90L, `91` = 91L, `92` = 92L,
`93` = 93L, `94` = 94L, `95` = 95L, `96` = 96L, `109` = 109L,
`110` = 110L, `111` = 111L, `112` = 112L, `113` = 113L, `114` = 114L,
`115` = 115L, `116` = 116L, `117` = 117L, `118` = 118L, `119` = 119L,
`120` = 120L, `121` = 121L, `122` = 122L, `123` = 123L, `124` = 124L,
`125` = 125L, `126` = 126L, `127` = 127L, `128` = 128L, `129` = 129L,
`130` = 130L, `131` = 131L, `132` = 132L, `145` = 145L, `146` = 146L,
`147` = 147L, `148` = 148L, `149` = 149L, `150` = 150L, `151` = 151L,
`152` = 152L, `153` = 153L, `154` = 154L, `155` = 155L, `156` = 156L,
`157` = 157L, `158` = 158L, `159` = 159L, `160` = 160L, `161` = 161L,
`162` = 162L, `163` = 163L, `164` = 164L, `165` = 165L, `166` = 166L,
`167` = 167L, `168` = 168L, `181` = 181L, `182` = 182L, `183` = 183L,
`184` = 184L, `185` = 185L, `186` = 186L, `187` = 187L, `188` = 188L,
`189` = 189L, `190` = 190L, `191` = 191L, `192` = 192L, `193` = 193L,
`194` = 194L, `195` = 195L, `196` = 196L, `197` = 197L, `198` = 198L,
`199` = 199L, `200` = 200L, `201` = 201L, `202` = 202L, `203` = 203L,
`204` = 204L, `217` = 217L, `218` = 218L, `219` = 219L, `220` = 220L,
`221` = 221L, `222` = 222L, `223` = 223L, `224` = 224L, `225` = 225L,
`226` = 226L, `227` = 227L, `228` = 228L, `229` = 229L, `230` = 230L,
`231` = 231L, `232` = 232L, `233` = 233L, `234` = 234L, `235` = 235L,
`236` = 236L, `237` = 237L, `238` = 238L, `239` = 239L, `240` = 240L,
`253` = 253L, `254` = 254L, `255` = 255L, `256` = 256L, `257` = 257L,
`258` = 258L, `259` = 259L, `260` = 260L, `261` = 261L, `262` = 262L,
`263` = 263L, `264` = 264L, `265` = 265L, `266` = 266L, `267` = 267L,
`268` = 268L, `269` = 269L, `270` = 270L, `271` = 271L, `272` = 272L,
`273` = 273L, `274` = 274L, `275` = 275L, `276` = 276L, `289` = 289L,
`290` = 290L, `291` = 291L, `292` = 292L, `293` = 293L, `294` = 294L,
`295` = 295L, `296` = 296L, `297` = 297L, `298` = 298L, `299` = 299L,
`300` = 300L, `301` = 301L, `302` = 302L, `303` = 303L, `304` = 304L,
`305` = 305L, `306` = 306L, `307` = 307L, `308` = 308L, `309` = 309L,
`310` = 310L, `311` = 311L, `312` = 312L, `325` = 325L, `326` = 326L,
`327` = 327L, `328` = 328L, `329` = 329L, `330` = 330L, `331` = 331L,
`332` = 332L, `333` = 333L, `334` = 334L, `335` = 335L, `336` = 336L,
`337` = 337L, `338` = 338L, `339` = 339L, `340` = 340L, `341` = 341L,
`342` = 342L, `343` = 343L, `344` = 344L, `345` = 345L, `346` = 346L,
`347` = 347L, `348` = 348L), class = "omit"))
Model code:
wage_hc_formula <- as.formula("Head_Count_Wages_Actual ~ Month_Factor + Quarter_Factor + Hours_12 + Hours_13 + Hours_14 + Hours_15 + Hours_16 + Hours_17 + Hours_18 + Hours_19 + Hours_20 + Hours_21 + Hours_22 + Hours_23 + Hours_24")
Fitting the model :
svm(formula = wage_hc_formula, data = mydata, kernel = 'linear',type='eps-regression',scale=F)
Я получил это сообщение об ошибке ниже.
Error in predict.svm(ret, xhold, decision.values = TRUE) :
Model is empty!
Я знаю, что технически мы должны просто обойти модель SVM, но если мы попытаемся подобрать модель, как мне это сделать?