Error: Model generated OK but when running the autogenerated project and trying to predict get error: Schema mismatch for input column '****': expected String or vector of String, got Single ' #5799
Description
System information
- OS version/distro: Windows 10 Enterprise
- .NET Version (eg., dotnet --info):
- Microsoft Visual Studio Community 2019 (2)
Version 16.9.4
VisualStudio.16.Release/16.9.4+31205.134
Microsoft .NET Framework
Version 4.8.03752
Installed Version: Community
Issue
-
What did you do?
-
I just created a simple model with ML.NET Model Builder (Value Prediction Scenario) and trained during an hour and the result was ok. Log below.
-
The data is on a simple database table where all the columns are of float type and just one of them (the table id) is integer. (I even tried to change the data type to decimal but the error is the same). The columns for the data are float and the prediction column is float also.
-
As the training was OK I added the autogenerated projects to my project to predict the results.
-
**What happened?
-
When executing the prediction code I got the error: "Schema mismatch for input column 'media1NecFisiologica': expected String or vector of String, got Single (Parameter 'inputSchema')
But that column is a float type on the table and I send a float number to try to predict.
I also tried to change teh data type to decimal just in case but I got the same error. -
What did you expect?
-
A prediction Result as a float number
Source code / logs
Trainer RSquared Absolute-loss Squared-loss RMS-loss Duration #Iteration |
Schema mismatch for input column 'media1NecFisiologica_tf_CharExtractor': expected Expected known-size vector of Single, got Vector
Nombre del parámetro: inputSchema
Schema mismatch for input column 'media1NecFisiologica_tf_CharExtractor': expected Expected known-size vector of Single, got Vector
Nombre del parámetro: inputSchema
Schema mismatch for input column 'media1NecFisiologica_tf_CharExtractor': expected Expected known-size vector of Single, got Vector
Nombre del parámetro: inputSchema
| Trainer RSquared Absolute-loss Squared-loss RMS-loss Duration #Iteration |
Schema mismatch for input column 'media1NecFisiologica_tf_CharExtractor': expected Expected known-size vector of Single, got Vector
Nombre del parámetro: inputSchema
Schema mismatch for input column 'media1NecFisiologica_tf_CharExtractor': expected Expected known-size vector of Single, got Vector
Nombre del parámetro: inputSchema
Schema mismatch for input column 'media1NecFisiologica_tf_CharExtractor': expected Expected known-size vector of Single, got Vector
Nombre del parámetro: inputSchema
| Trainer RSquared Absolute-loss Squared-loss RMS-loss Duration #Iteration |
Schema mismatch for input column 'media1NecFisiologica_tf_CharExtractor': expected Expected known-size vector of Single, got Vector
Nombre del parámetro: inputSchema
Schema mismatch for input column 'media1NecFisiologica_tf_CharExtractor': expected Expected known-size vector of Single, got Vector
Nombre del parámetro: inputSchema
Schema mismatch for input column 'media1NecFisiologica_tf_CharExtractor': expected Expected known-size vector of Single, got Vector
Nombre del parámetro: inputSchema
| Trainer RSquared Absolute-loss Squared-loss RMS-loss Duration #Iteration |
|1 SdcaRegression 0.8644 8.19 122.32 10.95 29.4 1 |
|2 LightGbmRegression 0.8366 9.04 154.20 12.23 20.7 2 |
===============================================Experiment Results=================================================
| Summary |
|ML Task: regression |
|Dataset: C:\Users\gpinto\AppData\Local\Temp\2e8ae3e9-3c87-412c-a3b0-71332ef45f9f.csv |
|Label : CalidadPonderada |
|Total experiment time : 50.0977468 Secs |
|Total number of models explored: 2 |
| Top 2 models explored |
| Trainer RSquared Absolute-loss Squared-loss RMS-loss Duration #Iteration |
|1 SdcaRegression 0.8644 8.19 122.32 10.95 29.4 1 |
|2 LightGbmRegression 0.8366 9.04 154.20 12.23 20.7 2 |
Generate project
Generate project success
| Trainer RSquared Absolute-loss Squared-loss RMS-loss Duration #Iteration |
|1 SdcaRegression 0.8649 8.17 122.00 10.93 27.4 1 |
|2 LightGbmRegression 0.8366 9.04 154.20 12.23 20.3 2 |
|3 FastTreeRegression 0.8378 8.54 160.61 12.29 21.4 3 |
|4 FastTreeTweedieRegression 0.8016 9.19 191.07 13.59 19.2 4 |
|5 FastForestRegression 0.8159 9.95 175.07 13.13 17.4 5 |
|6 LbfgsPoissonRegression 0.3303 13.75 598.25 20.13 53.0 6 |
|7 OnlineGradientDescentRegression 0.2492 16.29 612.15 23.24 22.0 7 |
Cannot hold covariance matrix in memory with 65733 features
|8 SdcaRegression 0.7367 13.05 248.91 15.65 22.7 8 |
|9 LightGbmRegression 0.8154 10.39 175.24 13.15 17.5 9 |
|10 FastTreeRegression 0.5838 16.61 375.80 19.28 51.2 10 |
|11 SdcaRegression 0.7968 9.96 183.06 13.33 22.1 11 |
|12 LightGbmRegression 0.8262 10.11 165.66 12.76 20.6 12 |
|13 FastTreeRegression 0.7938 11.63 192.44 13.82 15.7 13 |
|14 SdcaRegression 0.8496 8.59 135.00 11.47 21.1 14 |
|15 LightGbmRegression 0.8119 10.60 178.37 13.28 21.0 15 |
|16 FastTreeRegression 0.0219 25.61 850.75 29.03 16.4 16 |
|17 SdcaRegression 0.8482 8.72 135.13 11.56 21.1 17 |
|18 LightGbmRegression 0.7535 12.95 238.81 15.33 15.7 18 |
|19 FastTreeRegression -3.6652 56.45 4047.48 63.38 15.6 19 |
|20 SdcaRegression 0.8055 9.66 171.63 12.98 22.6 20 |
|21 LightGbmRegression 0.8422 8.75 149.74 12.08 21.7 21 |
|22 FastTreeRegression 0.8462 8.13 152.09 11.90 33.9 22 |
|23 SdcaRegression 0.8592 8.47 127.37 11.20 26.9 23 |
|24 LightGbmRegression 0.8270 9.01 168.11 12.71 20.1 24 |
|25 FastTreeRegression 0.8506 8.35 148.39 11.73 28.2 25 |
|26 SdcaRegression 0.8310 9.45 154.09 12.25 21.2 26 |
|27 LightGbmRegression 0.8544 7.46 142.96 11.60 20.0 27 |
|28 FastTreeRegression -1.7848 43.73 2415.22 48.94 15.4 28 |
|29 SdcaRegression 0.7438 13.04 243.60 15.41 21.6 29 |
|30 LightGbmRegression 0.8409 9.48 155.66 12.24 21.5 30 |
|31 FastTreeRegression 0.8446 8.22 153.39 11.97 36.1 31 |
|32 SdcaRegression 0.8195 9.71 161.68 12.61 21.4 32 |
|33 LightGbmRegression 0.7946 11.45 203.38 13.98 16.2 33 |
|34 FastTreeRegression 0.6575 16.04 305.92 17.44 14.6 34 |
|35 SdcaRegression 0.8412 9.20 143.37 11.89 20.7 35 |
|36 LightGbmRegression 0.8489 8.15 150.42 11.84 19.6 36 |
|37 FastTreeRegression -4.1673 59.36 4483.62 66.70 15.4 37 |
|38 SdcaRegression 0.8599 8.34 126.30 11.13 24.4 38 |
|39 LightGbmRegression 0.7515 11.71 237.52 15.20 18.6 39 |
|40 FastTreeRegression 0.8180 10.14 177.66 13.04 17.8 40 |
|41 SdcaRegression 0.7195 13.71 270.08 16.14 21.7 41 |
|42 LightGbmRegression 0.8040 7.87 194.70 13.60 24.0 42 |
|43 FastTreeRegression -0.0586 26.38 918.63 30.15 14.5 43 |
|44 SdcaRegression 0.8096 10.83 181.32 13.29 22.5 44 |
|45 LightGbmRegression 0.7863 11.91 208.06 14.25 21.1 45 |
|46 FastTreeRegression 0.8305 9.80 159.89 12.58 15.9 46 |
|47 SdcaRegression 0.8306 9.59 154.23 12.35 21.2 47 |
|48 LightGbmRegression 0.8057 10.65 184.69 13.51 21.9 48 |
|49 FastTreeRegression 0.8488 8.95 143.53 11.86 20.2 49 |
|50 SdcaRegression 0.8347 9.06 147.54 11.96 21.0 50 |
|51 LightGbmRegression 0.4163 20.35 577.32 23.71 21.0 51 |
|52 FastTreeRegression -1.2516 38.80 1945.22 43.86 15.3 52 |
|53 SdcaRegression 0.8604 8.33 126.01 11.16 20.3 53 |
|54 LightGbmRegression 0.8188 10.26 171.36 13.02 20.6 54 |
|55 FastTreeRegression 0.7286 7.88 261.53 15.65 45.4 55 |
|56 SdcaRegression 0.7625 12.49 226.69 14.85 22.2 56 |
|57 LightGbmRegression 0.7910 9.93 194.88 13.83 19.1 57 |
|58 FastTreeRegression 0.8491 8.10 149.01 11.76 35.1 58 |
|59 SdcaRegression 0.7264 13.23 260.96 15.99 21.5 59 |
|60 LightGbmRegression 0.8215 9.93 171.02 12.94 17.0 60 |
|61 FastTreeRegression 0.8131 10.63 177.74 13.25 16.2 61 |
|62 SdcaRegression 0.8629 8.28 122.69 10.98 52.6 62 |
|63 LightGbmRegression 0.4212 19.47 561.64 23.50 16.1 63 |
|64 FastTreeRegression 0.8470 8.27 151.18 11.82 35.3 64 |
|65 SdcaRegression 0.6730 14.86 316.97 17.49 27.5 65 |
|66 LightGbmRegression 0.7195 14.07 271.88 16.28 17.0 66 |
|67 FastTreeRegression 0.8560 7.88 141.30 11.54 36.4 67 |
|68 SdcaRegression 0.8630 8.20 123.73 11.03 23.8 68 |
|69 LightGbmRegression 0.8298 9.86 163.01 12.62 25.0 69 |
|70 FastTreeRegression 0.8457 8.81 148.58 11.96 21.8 70 |
|71 SdcaRegression 0.8544 8.47 131.17 11.33 98.6 71 |
|72 LightGbmRegression 0.8233 10.21 168.45 12.87 24.9 72 |
|73 FastTreeRegression -0.4108 31.03 1224.41 34.83 23.7 73 |
|74 SdcaRegression 0.7617 12.58 226.44 14.82 101.5 74 |
|75 LightGbmRegression 0.8394 8.33 158.66 12.12 23.4 75 |
|76 FastTreeRegression 0.8528 8.22 144.35 11.59 33.0 76 |
|77 SdcaRegression 0.8581 8.48 128.07 11.24 58.4 77 |
|78 LightGbmRegression 0.8222 10.24 169.35 12.91 23.5 78 |
|79 FastTreeRegression -0.0267 26.43 890.02 29.67 20.5 79 |
|80 SdcaRegression 0.8384 8.93 146.73 11.98 23.9 80 |
|81 LightGbmRegression 0.8473 8.16 153.38 11.88 26.7 81 |
|82 FastTreeRegression 0.7375 13.90 236.99 15.36 21.0 82 |
|83 SdcaRegression 0.8397 9.17 145.19 11.97 23.9 83 |
|84 LightGbmRegression 0.8431 8.41 154.81 12.06 22.8 84 |
|85 FastTreeRegression 0.8429 8.40 154.52 12.03 35.4 85 |
|86 SdcaRegression 0.8624 8.22 124.33 11.04 33.6 86 |
|87 LightGbmRegression 0.8398 8.90 152.70 12.19 24.2 87 |
|88 FastTreeRegression 0.8567 8.13 140.06 11.49 34.2 88 |
|89 SdcaRegression 0.8448 8.71 138.68 11.63 112.1 89 |
|90 LightGbmRegression 0.8418 8.79 149.93 12.01 21.9 90 |
|91 FastTreeRegression 0.8049 11.31 181.68 13.34 23.2 91 |
|92 SdcaRegression 0.8623 8.22 124.47 11.05 33.8 92 |
|93 LightGbmRegression 0.8340 9.47 164.02 12.49 19.1 93 |
|94 FastTreeRegression 0.6797 14.68 287.86 16.85 25.8 94 |
|95 SdcaRegression 0.8434 8.65 139.24 11.66 101.3 95 |
|96 LightGbmRegression 0.8458 8.35 152.16 11.93 23.0 96 |
|97 FastTreeRegression 0.8060 10.84 182.93 13.46 23.0 97 |
|98 SdcaRegression 0.8582 8.34 127.60 11.22 109.6 98 |
|99 LightGbmRegression 0.8449 8.40 151.56 11.99 23.2 99 |
|100 FastTreeRegression 0.8077 10.69 184.33 13.48 24.0 100 |
|101 SdcaRegression 0.8554 8.55 130.77 11.33 117.3 101 |
|102 LightGbmRegression 0.8467 7.62 150.63 11.87 23.4 102 |
|103 FastTreeRegression 0.7937 11.69 192.58 13.83 24.3 103 |
|104 SdcaRegression 0.8659 8.20 121.59 10.92 111.4 104 |
|105 LightGbmRegression 0.8384 8.52 158.83 12.21 23.4 105 |
|106 FastTreeRegression 0.4617 18.82 472.77 21.63 24.2 106 |
|107 SdcaRegression 0.6918 14.24 298.76 16.97 25.7 107 |
|108 LightGbmRegression 0.8568 8.61 137.19 11.56 22.5 108 |
|109 FastTreeRegression -1.9868 45.26 2588.66 50.67 23.2 109 |
|110 SdcaRegression 0.8622 8.23 124.59 11.09 122.0 110 |
|111 LightGbmRegression 0.8462 8.27 151.88 11.95 24.9 111 |
|112 FastTreeRegression 0.4989 18.19 442.34 20.95 31.6 112 |
|113 SdcaRegression 0.8611 8.31 126.04 11.13 127.2 113 |
|114 LightGbmRegression 0.8462 8.27 151.88 11.95 26.0 114 |
|115 FastTreeRegression 0.7928 11.63 193.49 13.86 27.5 115 |
===============================================Experiment Results=================================================
| Summary |
|ML Task: regression |
|Dataset: C:\Users\gpinto\AppData\Local\Temp\2e8ae3e9-3c87-412c-a3b0-71332ef45f9f.csv |
|Label : CalidadPonderada |
|Total experiment time : 3568.9233011 Secs |
|Total number of models explored: 116 |
| Top 5 models explored |
| Trainer RSquared Absolute-loss Squared-loss RMS-loss Duration #Iteration |
|1 SdcaRegression 0.8659 8.20 121.59 10.92 111.4 1 |
|2 SdcaRegression 0.8649 8.17 122.00 10.93 27.4 2 |
|3 SdcaRegression 0.8630 8.20 123.73 11.03 23.8 3 |
|4 SdcaRegression 0.8629 8.28 122.69 10.98 52.6 4 |
|5 SdcaRegression 0.8624 8.22 124.33 11.04 33.6 5 |
Generate project
Generate project success
Predicted value: CalidadPonderada:
Add MLPrediccionesML.Model
| Trainer RSquared Absolute-loss Squared-loss RMS-loss Duration #Iteration |
|1 SdcaRegression 0.8654 8.17 121.62 10.92 27.5 1 |
|2 LightGbmRegression 0.8424 8.80 152.70 12.10 20.8 2 |
===============================================Experiment Results=================================================
| Summary |
|ML Task: regression |
|Dataset: C:\Users\gpinto\AppData\Local\Temp\2e8ae3e9-3c87-412c-a3b0-71332ef45f9f.csv |
|Label : CalidadPonderada |
|Total experiment time : 48.3019823 Secs |
|Total number of models explored: 2 |
| Top 2 models explored |
| Trainer RSquared Absolute-loss Squared-loss RMS-loss Duration #Iteration |
|1 SdcaRegression 0.8654 8.17 121.62 10.92 27.5 1 |
|2 LightGbmRegression 0.8424 8.80 152.70 12.10 20.8 2 |
Generate project
Generate project success
Predicted value: CalidadPonderada:
Add MLPrediccionesML.Model
| Trainer RSquared Absolute-loss Squared-loss RMS-loss Duration #Iteration |
|1 SdcaRegression 0.8657 8.19 121.55 10.92 28.4 1 |
|2 LightGbmRegression 0.8424 8.80 152.70 12.10 20.5 2 |
|3 FastTreeRegression 0.8406 8.53 158.32 12.17 23.7 3 |
|4 FastTreeTweedieRegression 0.7943 9.33 194.43 13.71 23.1 4 |
|5 FastForestRegression 0.8204 9.82 170.09 12.95 19.3 5 |
|6 LbfgsPoissonRegression 0.3929 13.55 541.52 19.63 56.5 6 |
|7 OnlineGradientDescentRegression 0.6245 14.53 366.25 18.59 20.9 7 |
Cannot hold covariance matrix in memory with 65728 features
|8 SdcaRegression 0.8130 10.41 175.87 13.15 21.6 8 |
|9 LightGbmRegression 0.3359 21.23 655.47 25.29 14.9 9 |
|10 FastTreeRegression 0.8598 8.35 135.75 11.37 21.1 10 |
|11 SdcaRegression 0.8645 8.18 122.38 10.96 24.7 11 |
|12 LightGbmRegression 0.8438 8.40 154.78 12.02 20.8 12 |
|13 FastTreeRegression 0.8238 10.40 166.86 12.81 14.5 13 |
|14 SdcaRegression 0.8374 8.91 146.05 11.99 23.0 14 |
|15 LightGbmRegression 0.8386 8.56 158.45 12.27 21.1 15 |
|16 FastTreeRegression 0.7276 14.33 246.36 15.66 14.0 16 |
|17 SdcaRegression 0.8326 9.32 151.02 12.17 22.1 17 |
|18 LightGbmRegression 0.8576 7.98 141.91 11.56 15.9 18 |
|19 FastTreeRegression -1.0589 37.95 1793.42 42.20 62.3 19 |
|20 SdcaRegression 0.7278 13.29 259.13 15.96 21.5 20 |
|21 LightGbmRegression 0.6624 15.28 333.03 18.02 21.0 21 |
|22 FastTreeRegression 0.8049 10.96 183.21 13.47 15.5 22 |
|23 SdcaRegression 0.7572 12.56 230.25 15.01 21.3 23 |
|24 LightGbmRegression 0.8392 9.41 152.71 12.26 16.1 24 |
|25 FastTreeRegression 0.8496 8.66 141.60 11.71 16.3 25 |
|26 SdcaRegression 0.5985 16.56 393.07 19.46 21.8 26 |
|27 LightGbmRegression 0.8332 7.94 162.23 12.45 18.8 27 |
|28 FastTreeRegression 0.8201 10.40 168.06 12.92 15.2 28 |
|29 SdcaRegression 0.8191 9.76 162.81 12.66 22.5 29 |
|30 LightGbmRegression 0.4528 18.93 530.97 22.85 18.8 30 |
|31 FastTreeRegression -3.5904 56.10 3984.62 62.89 22.3 31 |
|32 SdcaRegression 0.8536 8.79 132.83 11.46 27.2 32 |
|33 LightGbmRegression 0.8342 7.79 163.78 12.49 15.4 33 |
|34 FastTreeRegression 0.7708 12.66 216.31 14.65 14.5 34 |
|35 SdcaRegression 0.8560 8.45 129.82 11.26 85.5 35 |
|36 LightGbmRegression 0.8224 10.20 168.72 12.89 15.7 36 |
|37 FastTreeRegression 0.4014 20.18 523.34 22.76 16.2 37 |
|38 SdcaRegression 0.8014 10.04 181.29 13.34 21.4 38 |
|39 LightGbmRegression 0.7977 11.09 192.76 13.80 21.9 39 |
|40 FastTreeRegression 0.8512 8.16 146.85 11.70 31.3 40 |
|41 SdcaRegression 0.8529 8.73 132.37 11.38 20.8 41 |
|42 LightGbmRegression 0.8339 8.58 160.84 12.33 23.8 42 |
|43 FastTreeRegression -0.3284 29.61 1149.46 33.71 15.5 43 |
|44 SdcaRegression 0.8653 8.20 122.05 10.94 91.9 44 |
|45 LightGbmRegression 0.8278 9.90 165.14 12.69 21.5 45 |
|46 FastTreeRegression -0.5789 33.09 1365.82 36.77 16.0 46 |
|47 SdcaRegression 0.8509 8.62 133.01 11.44 21.4 47 |
|48 LightGbmRegression 0.8508 8.92 142.48 11.82 21.7 48 |
|49 FastTreeRegression 0.5245 17.57 430.75 20.64 72.5 49 |
|50 SdcaRegression 0.7459 12.86 240.54 15.41 21.4 50 |
|51 LightGbmRegression 0.8186 9.97 172.66 13.03 16.4 51 |
|52 FastTreeRegression 0.8420 8.52 154.16 12.00 19.4 52 |
|53 SdcaRegression 0.8518 8.59 135.56 11.52 93.2 53 |
|54 LightGbmRegression 0.8309 9.81 161.99 12.59 23.5 54 |
|55 FastTreeRegression 0.8659 8.41 129.43 11.21 23.3 55 |
|56 SdcaRegression 0.6953 13.98 292.84 16.93 22.1 56 |
|57 LightGbmRegression 0.8560 7.81 138.62 11.45 23.2 57 |
|58 FastTreeRegression 0.8306 9.83 163.37 12.51 32.4 58 |
|59 SdcaRegression 0.8547 8.48 130.94 11.36 21.7 59 |
|60 LightGbmRegression 0.8421 8.34 155.53 12.10 21.6 60 |
|61 FastTreeRegression 0.7990 9.35 194.04 13.52 39.1 61 |
|62 SdcaRegression 0.8508 8.96 136.62 11.56 25.5 62 |
|63 LightGbmRegression 0.8465 8.19 151.69 11.93 18.7 63 |
|64 FastTreeRegression 0.8056 8.70 189.36 13.19 44.8 64 |
|65 SdcaRegression 0.8003 11.08 191.60 13.63 24.9 65 |
|66 LightGbmRegression 0.7686 11.27 218.52 14.63 19.0 66 |
|67 FastTreeRegression 0.6368 16.47 322.67 17.90 20.3 67 |
|68 SdcaRegression 0.8648 8.16 122.19 10.94 28.7 68 |
|69 LightGbmRegression 0.7520 12.92 244.59 15.39 23.7 69 |
|70 FastTreeRegression 0.8460 8.45 150.92 11.91 25.7 70 |
|71 SdcaRegression 0.7625 12.71 226.12 14.84 108.8 71 |
|72 LightGbmRegression 0.8510 7.89 145.77 11.77 27.2 72 |
|73 FastTreeRegression 0.7851 12.15 199.38 14.08 23.1 73 |
|74 SdcaRegression 0.8539 8.57 131.62 11.32 80.4 74 |
|75 LightGbmRegression 0.8629 7.96 134.22 11.27 25.8 75 |
|76 FastTreeRegression 0.8616 7.97 135.40 11.20 34.0 76 |
|77 SdcaRegression 0.8627 8.24 123.28 11.00 51.4 77 |
|78 LightGbmRegression 0.8494 7.90 147.52 11.80 28.9 78 |
|79 FastTreeRegression 0.7255 13.74 247.15 15.61 22.4 79 |
|80 SdcaRegression 0.8646 8.18 122.33 10.95 32.6 80 |
|81 LightGbmRegression 0.4039 20.09 588.45 23.96 24.9 81 |
|82 FastTreeRegression 0.8590 8.45 136.41 11.45 25.2 82 |
|83 SdcaRegression 0.8500 8.49 132.32 11.38 26.9 83 |
|84 LightGbmRegression 0.8605 7.80 135.97 11.36 30.5 84 |
|85 FastTreeRegression 0.8541 9.00 140.38 11.68 24.5 85 |
|86 SdcaRegression 0.8584 8.46 127.47 11.21 53.1 86 |
|87 LightGbmRegression 0.8631 8.12 130.82 11.26 28.9 87 |
|88 FastTreeRegression 0.8506 8.19 148.28 11.72 48.3 88 |
|89 SdcaRegression 0.8596 8.34 125.62 11.09 52.2 89 |
|90 LightGbmRegression 0.7767 10.93 220.97 14.56 24.1 90 |
|91 FastTreeRegression 0.1890 23.36 703.88 26.38 24.3 91 |
|92 SdcaRegression 0.8594 8.39 127.46 11.20 86.8 92 |
|93 LightGbmRegression 0.8446 8.94 147.69 11.98 28.4 93 |
|94 FastTreeRegression 0.8496 8.21 149.27 11.74 48.7 94 |
|95 SdcaRegression 0.7845 11.62 210.47 14.22 26.9 95 |
|96 LightGbmRegression 0.8640 8.03 131.09 11.22 28.0 96 |
|97 FastTreeRegression 0.8552 8.77 136.86 11.62 27.3 97 |
|98 SdcaRegression 0.8603 8.37 126.63 11.16 119.4 98 |
|99 LightGbmRegression 0.8607 8.09 133.75 11.38 25.6 99 |
|100 FastTreeRegression 0.8426 9.33 148.04 12.06 27.3 100 |
|101 SdcaRegression 0.8255 8.94 152.83 12.14 27.1 101 |
|102 LightGbmRegression 0.8610 8.07 133.57 11.36 30.7 102 |
|103 FastTreeRegression 0.8663 8.34 128.64 11.19 34.3 103 |
|104 SdcaRegression 0.8544 8.52 131.17 11.38 29.9 104 |
|105 LightGbmRegression 0.8564 7.96 140.21 11.55 28.3 105 |
|106 FastTreeRegression 0.8591 8.48 137.49 11.46 35.3 106 |
|107 SdcaRegression 0.8579 8.57 128.42 11.23 126.6 107 |
|108 LightGbmRegression 0.8655 7.95 129.26 11.14 22.9 108 |
|109 FastTreeRegression 0.8506 8.02 148.10 11.72 51.6 109 |
|110 SdcaRegression 0.8600 8.37 126.93 11.17 126.8 110 |
===============================================Experiment Results=================================================
| Summary |
|ML Task: regression |
|Dataset: C:\Users\gpinto\AppData\Local\Temp\2e8ae3e9-3c87-412c-a3b0-71332ef45f9f.csv |
|Label : CalidadPonderada |
|Total experiment time : 3579.5917172 Secs |
|Total number of models explored: 111 |
| Top 5 models explored |
| Trainer RSquared Absolute-loss Squared-loss RMS-loss Duration #Iteration |
|1 FastTreeRegression 0.8663 8.34 128.64 11.19 34.3 1 |
|2 FastTreeRegression 0.8659 8.41 129.43 11.21 23.3 2 |
|3 SdcaRegression 0.8657 8.19 121.55 10.92 28.4 3 |
|4 LightGbmRegression 0.8655 7.95 129.26 11.14 22.9 4 |
|5 SdcaRegression 0.8653 8.20 122.05 10.94 91.9 5 |
Generate project
Generate project success
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