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SVD_model_result.txt
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Finish loading data
===== evaluating parameter choice 50 5 0.002 0.2
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1798 1.1822 1.1779 1.1776 1.1836 1.1720 1.1823 1.1808 1.1785 1.1766 1.1791 0.0032
MAE (testset) 0.9532 0.9530 0.9507 0.9514 0.9583 0.9473 0.9534 0.9517 0.9526 0.9498 0.9521 0.0027
Fit time 5.49 5.71 5.34 5.47 5.42 4.98 5.01 5.02 5.04 4.94 5.24 0.26
Test time 0.50 0.54 0.46 0.45 0.46 0.43 0.54 0.44 0.44 0.45 0.47 0.04
===== evaluating parameter choice 50 5 0.002 0.4
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1758 1.1746 1.1821 1.1826 1.1833 1.1856 1.1839 1.1824 1.1841 1.1861 1.1821 0.0036
MAE (testset) 0.9532 0.9491 0.9563 0.9571 0.9550 0.9578 0.9580 0.9571 0.9574 0.9576 0.9559 0.0027
Fit time 4.99 4.96 4.98 5.01 5.22 4.97 5.38 5.08 5.07 4.98 5.06 0.13
Test time 0.55 0.45 0.44 0.44 0.44 0.53 0.42 0.52 0.46 0.43 0.47 0.05
===== evaluating parameter choice 50 5 0.002 0.8
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1913 1.1970 1.1864 1.1893 1.1892 1.1899 1.1883 1.1855 1.1848 1.1842 1.1886 0.0036
MAE (testset) 0.9665 0.9699 0.9623 0.9635 0.9629 0.9636 0.9636 0.9624 0.9627 0.9597 0.9637 0.0026
Fit time 5.42 5.63 5.50 5.32 5.51 5.32 5.23 5.25 5.11 5.26 5.35 0.15
Test time 0.65 0.49 0.53 0.47 0.46 0.44 0.43 0.45 0.42 0.59 0.49 0.07
===== evaluating parameter choice 50 5 0.005 0.2
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1451 1.1414 1.1476 1.1429 1.1445 1.1394 1.1396 1.1407 1.1419 1.1445 1.1428 0.0025
MAE (testset) 0.9203 0.9163 0.9212 0.9192 0.9206 0.9175 0.9165 0.9171 0.9197 0.9200 0.9188 0.0017
Fit time 5.05 4.92 5.50 5.60 5.69 5.00 5.15 5.45 5.07 5.51 5.29 0.27
Test time 0.45 0.44 0.53 0.47 0.56 0.53 0.52 0.42 0.43 0.45 0.48 0.05
===== evaluating parameter choice 50 5 0.005 0.4
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1454 1.1522 1.1466 1.1442 1.1502 1.1481 1.1430 1.1461 1.1502 1.1500 1.1476 0.0028
MAE (testset) 0.9230 0.9286 0.9247 0.9223 0.9282 0.9258 0.9228 0.9226 0.9267 0.9255 0.9250 0.0022
Fit time 4.83 4.97 5.50 5.59 5.75 5.31 5.17 5.10 5.37 5.40 5.30 0.27
Test time 0.43 0.45 0.67 0.62 0.47 0.45 0.45 0.44 0.59 0.55 0.51 0.08
===== evaluating parameter choice 50 5 0.005 0.8
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1536 1.1571 1.1504 1.1605 1.1631 1.1607 1.1570 1.1642 1.1584 1.1549 1.1580 0.0041
MAE (testset) 0.9317 0.9363 0.9326 0.9393 0.9410 0.9377 0.9365 0.9417 0.9369 0.9338 0.9367 0.0032
Fit time 4.86 5.33 5.38 5.81 6.15 5.65 5.49 5.85 5.95 5.83 5.63 0.36
Test time 0.45 0.46 0.48 0.53 0.55 0.50 0.48 0.48 0.57 0.72 0.52 0.08
===== evaluating parameter choice 50 5 0.01 0.2
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1208 1.1271 1.1248 1.1213 1.1175 1.1251 1.1237 1.1197 1.1207 1.1147 1.1215 0.0036
MAE (testset) 0.8969 0.9005 0.8999 0.8965 0.8932 0.8988 0.8966 0.8966 0.8976 0.8908 0.8967 0.0028
Fit time 5.41 5.28 5.66 5.47 5.60 5.60 5.11 5.19 5.24 5.08 5.36 0.20
Test time 0.47 0.49 0.63 0.47 0.54 0.48 0.48 0.45 0.51 0.47 0.50 0.05
===== evaluating parameter choice 50 5 0.01 0.4
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1263 1.1317 1.1286 1.1350 1.1229 1.1285 1.1225 1.1256 1.1255 1.1280 1.1275 0.0036
MAE (testset) 0.9045 0.9103 0.9065 0.9109 0.9022 0.9063 0.9000 0.9031 0.9042 0.9049 0.9053 0.0032
Fit time 4.89 5.09 5.07 5.09 5.11 5.10 5.08 5.15 5.12 5.25 5.09 0.08
Test time 0.45 0.57 0.46 0.46 0.46 0.45 0.56 0.44 0.45 0.46 0.48 0.04
===== evaluating parameter choice 50 5 0.01 0.8
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1412 1.1367 1.1420 1.1395 1.1377 1.1357 1.1416 1.1415 1.1466 1.1414 1.1404 0.0030
MAE (testset) 0.9220 0.9173 0.9242 0.9178 0.9178 0.9181 0.9219 0.9221 0.9242 0.9194 0.9205 0.0026
Fit time 4.89 5.15 5.17 5.17 5.07 5.08 5.20 5.17 5.12 5.18 5.12 0.09
Test time 0.45 0.46 0.47 0.44 0.45 0.45 0.46 0.44 0.56 0.45 0.46 0.03
===== evaluating parameter choice 100 5 0.002 0.2
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1756 1.1751 1.1800 1.1795 1.1814 1.1805 1.1736 1.1872 1.1828 1.1825 1.1798 0.0039
MAE (testset) 0.9491 0.9499 0.9519 0.9518 0.9549 0.9524 0.9486 0.9572 0.9552 0.9550 0.9526 0.0028
Fit time 7.39 7.82 7.72 7.66 7.68 7.64 7.80 7.62 9.30 8.27 7.89 0.52
Test time 0.45 0.47 0.47 0.45 0.45 0.56 0.55 0.45 0.84 0.49 0.52 0.11
===== evaluating parameter choice 100 5 0.002 0.4
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1792 1.1880 1.1890 1.1815 1.1767 1.1777 1.1816 1.1822 1.1864 1.1849 1.1827 0.0040
MAE (testset) 0.9528 0.9605 0.9621 0.9544 0.9515 0.9518 0.9571 0.9557 0.9577 0.9578 0.9561 0.0034
Fit time 8.05 8.47 8.34 8.31 8.28 8.23 8.50 8.43 8.68 8.14 8.34 0.17
Test time 0.60 0.53 0.50 0.49 0.49 0.50 0.49 0.48 0.48 0.59 0.52 0.04
===== evaluating parameter choice 100 5 0.002 0.8
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1896 1.1929 1.1874 1.1913 1.1915 1.1893 1.1831 1.1873 1.1867 1.1899 1.1889 0.0027
MAE (testset) 0.9667 0.9663 0.9619 0.9665 0.9650 0.9634 0.9594 0.9627 0.9606 0.9651 0.9638 0.0024
Fit time 8.06 8.86 8.21 8.32 8.14 8.20 8.24 8.51 7.94 7.58 8.21 0.32
Test time 0.56 0.48 0.51 0.49 0.47 0.53 0.51 0.65 0.43 0.54 0.52 0.06
===== evaluating parameter choice 100 5 0.005 0.2
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1482 1.1460 1.1412 1.1386 1.1414 1.1448 1.1402 1.1423 1.1442 1.1430 1.1430 0.0027
MAE (testset) 0.9233 0.9206 0.9178 0.9162 0.9181 0.9202 0.9175 0.9186 0.9184 0.9185 0.9189 0.0019
Fit time 7.21 7.40 7.57 7.41 7.51 7.94 7.55 7.61 8.11 7.60 7.59 0.25
Test time 0.56 0.45 0.45 0.44 0.46 0.47 0.45 0.45 0.44 0.45 0.46 0.03
===== evaluating parameter choice 100 5 0.005 0.4
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1430 1.1485 1.1446 1.1497 1.1441 1.1492 1.1522 1.1525 1.1422 1.1511 1.1477 0.0037
MAE (testset) 0.9211 0.9251 0.9236 0.9274 0.9214 0.9247 0.9301 0.9299 0.9190 0.9291 0.9251 0.0037
Fit time 7.84 7.67 7.89 9.32 8.05 7.93 7.73 7.87 7.83 7.94 8.01 0.45
Test time 0.44 0.43 0.46 0.72 0.47 0.67 0.56 0.48 0.49 0.46 0.52 0.10
===== evaluating parameter choice 100 5 0.005 0.8
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1628 1.1593 1.1571 1.1537 1.1579 1.1550 1.1529 1.1626 1.1558 1.1641 1.1581 0.0038
MAE (testset) 0.9407 0.9381 0.9362 0.9318 0.9361 0.9341 0.9358 0.9404 0.9328 0.9425 0.9369 0.0033
Fit time 8.14 7.78 7.61 7.56 7.52 7.49 7.39 7.64 7.55 7.62 7.63 0.19
Test time 0.56 0.58 0.44 0.48 0.45 0.53 0.55 0.55 0.45 0.45 0.50 0.05
===== evaluating parameter choice 100 5 0.01 0.2
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1202 1.1219 1.1196 1.1228 1.1216 1.1241 1.1208 1.1281 1.1176 1.1203 1.1217 0.0027
MAE (testset) 0.8973 0.8981 0.8923 0.8977 0.8960 0.8990 0.8958 0.9030 0.8941 0.8942 0.8968 0.0029
Fit time 7.38 7.69 7.49 7.83 7.86 7.56 7.82 7.79 8.08 7.82 7.73 0.20
Test time 0.44 0.58 0.54 0.48 0.51 0.44 0.52 0.57 0.54 0.44 0.51 0.05
===== evaluating parameter choice 100 5 0.01 0.4
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1238 1.1265 1.1318 1.1289 1.1243 1.1279 1.1267 1.1257 1.1300 1.1309 1.1277 0.0026
MAE (testset) 0.9016 0.9041 0.9086 0.9077 0.9045 0.9055 0.9043 0.9032 0.9068 0.9091 0.9055 0.0023
Fit time 7.46 7.45 7.51 7.46 7.44 7.74 7.80 7.61 7.77 7.90 7.61 0.17
Test time 0.43 0.45 0.45 0.46 0.46 0.62 0.55 0.47 0.44 0.45 0.48 0.06
===== evaluating parameter choice 100 5 0.01 0.8
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1426 1.1372 1.1404 1.1388 1.1445 1.1405 1.1429 1.1402 1.1410 1.1392 1.1407 0.0020
MAE (testset) 0.9220 0.9181 0.9215 0.9184 0.9243 0.9199 0.9219 0.9219 0.9202 0.9180 0.9206 0.0019
Fit time 7.65 7.48 8.15 7.43 7.49 7.98 8.14 7.85 7.57 7.55 7.73 0.26
Test time 0.54 0.55 0.45 0.45 0.47 0.44 0.67 0.64 0.46 0.48 0.52 0.08
===== evaluating parameter choice 150 5 0.002 0.2
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1867 1.1748 1.1838 1.1770 1.1830 1.1830 1.1782 1.1830 1.1780 1.1775 1.1805 0.0037
MAE (testset) 0.9574 0.9471 0.9546 0.9500 0.9558 0.9547 0.9524 0.9542 0.9518 0.9506 0.9529 0.0029
Fit time 10.13 10.11 10.10 10.24 9.85 10.19 10.13 10.23 11.70 11.30 10.40 0.57
Test time 0.45 0.57 0.48 0.46 0.46 0.46 0.57 0.46 0.51 0.54 0.50 0.05
===== evaluating parameter choice 150 5 0.002 0.4
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1812 1.1875 1.1812 1.1825 1.1813 1.1777 1.1847 1.1835 1.1894 1.1818 1.1831 0.0032
MAE (testset) 0.9550 0.9598 0.9545 0.9562 0.9548 0.9525 0.9573 0.9560 0.9618 0.9557 0.9563 0.0026
Fit time 10.49 10.78 10.42 10.46 10.56 10.50 10.55 10.33 10.24 10.31 10.46 0.15
Test time 0.46 0.48 0.47 0.48 0.49 0.53 0.48 0.49 0.58 0.48 0.49 0.03
===== evaluating parameter choice 150 5 0.002 0.8
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1852 1.1867 1.1841 1.1871 1.1932 1.1923 1.1887 1.1914 1.1869 1.1946 1.1890 0.0034
MAE (testset) 0.9615 0.9609 0.9600 0.9634 0.9680 0.9652 0.9630 0.9652 0.9630 0.9677 0.9638 0.0026
Fit time 10.20 10.10 10.37 10.38 10.79 10.46 10.37 10.40 10.15 10.20 10.34 0.19
Test time 0.46 0.45 0.44 0.46 0.52 0.58 0.59 0.46 0.46 0.47 0.49 0.05
===== evaluating parameter choice 150 5 0.005 0.2
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1429 1.1442 1.1453 1.1411 1.1386 1.1447 1.1448 1.1401 1.1449 1.1448 1.1431 0.0022
MAE (testset) 0.9175 0.9196 0.9188 0.9182 0.9170 0.9205 0.9207 0.9169 0.9204 0.9211 0.9191 0.0015
Fit time 10.04 10.03 10.92 11.24 10.24 10.51 10.37 10.29 10.15 10.20 10.40 0.37
Test time 0.55 0.47 0.53 0.77 0.46 0.52 0.49 0.48 0.47 0.62 0.54 0.09
===== evaluating parameter choice 150 5 0.005 0.4
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1493 1.1450 1.1477 1.1495 1.1480 1.1458 1.1460 1.1529 1.1489 1.1438 1.1477 0.0025
MAE (testset) 0.9266 0.9240 0.9244 0.9265 0.9257 0.9224 0.9230 0.9313 0.9255 0.9208 0.9250 0.0027
Fit time 9.69 9.80 10.16 10.29 10.46 10.90 10.30 9.90 10.11 10.26 10.19 0.33
Test time 0.54 0.45 0.49 0.45 0.49 0.48 0.50 0.47 0.46 0.61 0.49 0.05
===== evaluating parameter choice 150 5 0.005 0.8
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1570 1.1601 1.1564 1.1597 1.1549 1.1555 1.1520 1.1591 1.1619 1.1642 1.1581 0.0034
MAE (testset) 0.9370 0.9367 0.9350 0.9375 0.9341 0.9364 0.9304 0.9375 0.9392 0.9412 0.9365 0.0028
Fit time 10.33 10.13 10.31 10.68 10.72 10.13 10.24 10.16 10.19 10.19 10.31 0.21
Test time 0.55 0.45 0.58 0.45 0.45 0.44 0.48 0.46 0.48 0.49 0.48 0.04
===== evaluating parameter choice 150 5 0.01 0.2
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1187 1.1169 1.1266 1.1239 1.1199 1.1170 1.1251 1.1200 1.1258 1.1223 1.1216 0.0034
MAE (testset) 0.8940 0.8948 0.8998 0.9004 0.8951 0.8932 0.8983 0.8959 0.8989 0.8976 0.8968 0.0024
Fit time 9.82 10.37 10.15 9.87 10.01 9.96 9.89 10.42 10.86 10.40 10.18 0.32
Test time 0.46 0.45 0.45 0.46 0.44 0.55 0.55 0.51 0.60 0.44 0.49 0.05
===== evaluating parameter choice 150 5 0.01 0.4
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1246 1.1250 1.1253 1.1271 1.1269 1.1242 1.1303 1.1276 1.1290 1.1331 1.1273 0.0027
MAE (testset) 0.9029 0.9026 0.9045 0.9069 0.9038 0.9013 0.9072 0.9057 0.9051 0.9112 0.9051 0.0027
Fit time 10.16 10.50 10.10 10.85 10.30 9.87 9.90 10.75 10.98 10.40 10.38 0.37
Test time 0.57 0.57 0.47 0.47 0.45 0.44 0.58 0.66 0.48 0.46 0.51 0.07
===== evaluating parameter choice 150 5 0.01 0.8
Evaluating RMSE, MAE of algorithm SVD on 10 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 Mean Std
RMSE (testset) 1.1441 1.1420 1.1411 1.1402 1.1362 1.1381 1.1391 1.1424 1.1430 1.1382 1.1404 0.0024
MAE (testset) 0.9231 0.9204 0.9200 0.9204 0.9166 0.9183 0.9193 0.9232 0.9226 0.9199 0.9204 0.0020
Fit time 9.98 10.97 10.52 10.55 10.06 10.03 10.25 10.07 10.82 10.96 10.42 0.38
Test time 0.47 0.58 0.46 0.47 0.46 0.45 0.57 0.46 0.47 0.50 0.49 0.05
{(50, 5, 0.002, 0.2): {'test_rmse': array([1.1797959 , 1.18219191, 1.17790811, 1.17757401, 1.18364658,
1.17204691, 1.18233616, 1.18077766, 1.17851658, 1.17663058]), 'test_mae': array([0.95317349, 0.95297026, 0.95068804, 0.95143903, 0.9583133 ,
0.9473428 , 0.95337174, 0.95171475, 0.95255934, 0.94983661]), 'fit_time': (5.4912872314453125, 5.712738275527954, 5.337758779525757, 5.4724016189575195, 5.417543888092041, 4.980713605880737, 5.005612373352051, 5.0213189125061035, 5.041527509689331, 4.94178318977356), 'test_time': (0.49767112731933594, 0.5355930328369141, 0.4567854404449463, 0.45076942443847656, 0.45977258682250977, 0.4338390827178955, 0.5385613441467285, 0.44085073471069336, 0.4408421516418457, 0.4468057155609131)}, (50, 5, 0.002, 0.4): {'test_rmse': array([1.1758432 , 1.17456217, 1.18213528, 1.18260149, 1.18327726,
1.18557868, 1.18390998, 1.18240945, 1.18405822, 1.1861255 ]), 'test_mae': array([0.95324994, 0.94908049, 0.95626323, 0.95705655, 0.95495124,
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