Pipeline
Compete Name:rf-tfidf
Pipeline Name:
Experimental Results
| Pipeline ID | Execution time | Memory | Score | Library & Version |
|---|---|---|---|---|
| 35699 | 136.3521944 | 327.0165815353 | 0.6151303347041098 | numpy,1.17.4 |
| 35699 | 136.3521944 | 327.0165815353 | 0.6151303347041098 | scikit-learn,1.0.1 |
| 35699 | 137.64929569999998 | 329.8752508163 | 0.6151303347041098 | numpy,1.17.4 |
| 35699 | 137.64929569999998 | 329.8752508163 | 0.6151303347041098 | scikit-learn,0.24.2 |
| 35699 | 136.7356805 | 329.6776161194 | 0.6151303347041098 | numpy,1.17.4 |
| 35699 | 136.7356805 | 329.6776161194 | 0.6151303347041098 | scikit-learn,0.23.2 |
| 35699 | 136.9660145 | 326.0462770462 | 0.6151303347041098 | numpy,1.17.4 |
| 35699 | 136.9660145 | 326.0462770462 | 0.6151303347041098 | scikit-learn,0.22.1 |
| 35699 | 135.0736976 | 326.0419397354 | 0.6151303347041098 | numpy,1.17.4 |
| 35699 | 135.0736976 | 326.0419397354 | 0.6151303347041098 | scikit-learn,0.22 |
| 35699 | 134.8913618 | 325.9651145935 | 0.6151303347041098 | numpy,1.17.4 |
| 35699 | 134.8913618 | 325.9651145935 | 0.6151303347041098 | scikit-learn,0.21.3 |
| 35699 | 133.7444255 | 326.3333454132 | 0.6067516258908594 | numpy,1.17.4 |
| 35699 | 133.7444255 | 326.3333454132 | 0.6067516258908594 | scikit-learn,0.20.3 |
| 35699 | 134.2079434 | 325.1119842529 | 0.6067516258908594 | numpy,1.17.4 |
| 35699 | 134.2079434 | 325.1119842529 | 0.6067516258908594 | scikit-learn,0.19.2 |
| 35699 | 137.4174278 | 327.9153699875 | 0.6151303347041098 | numpy,1.18.5 |
| 35699 | 137.4174278 | 327.9153699875 | 0.6151303347041098 | scikit-learn,1.0.1 |
| 35699 | 137.6070652 | 330.7804069519 | 0.6151303347041098 | numpy,1.18.5 |
| 35699 | 137.6070652 | 330.7804069519 | 0.6151303347041098 | scikit-learn,0.24.2 |
| 35699 | 137.8994288 | 330.5823392868 | 0.6151303347041098 | numpy,1.18.5 |
| 35699 | 137.8994288 | 330.5823392868 | 0.6151303347041098 | scikit-learn,0.23.2 |
| 35699 | 137.498976 | 326.9501209259 | 0.6151303347041098 | numpy,1.18.5 |
| 35699 | 137.498976 | 326.9501209259 | 0.6151303347041098 | scikit-learn,0.22.1 |
| 35699 | 135.37636999999998 | 326.946805954 | 0.6151303347041098 | numpy,1.18.5 |
| 35699 | 135.37636999999998 | 326.946805954 | 0.6151303347041098 | scikit-learn,0.22 |
| 35699 | 136.9799352 | 326.8688488007 | 0.6151303347041098 | numpy,1.18.5 |
| 35699 | 136.9799352 | 326.8688488007 | 0.6151303347041098 | scikit-learn,0.21.3 |
| 35699 | 135.4110211 | 327.2382717133 | 0.6067516258908594 | numpy,1.18.5 |
| 35699 | 135.4110211 | 327.2382717133 | 0.6067516258908594 | scikit-learn,0.20.3 |
| 35699 | 133.7403592 | 326.0161981583 | 0.6067516258908594 | numpy,1.18.5 |
| 35699 | 133.7403592 | 326.0161981583 | 0.6067516258908594 | scikit-learn,0.19.2 |
| 35699 | 137.9497287 | 328.0366859436 | 0.6151303347041098 | numpy,1.19.5 |
| 35699 | 137.9497287 | 328.0366859436 | 0.6151303347041098 | scikit-learn,1.0.1 |
| 35699 | 136.2274277 | 330.895450592 | 0.6151303347041098 | numpy,1.19.5 |
| 35699 | 136.2274277 | 330.895450592 | 0.6151303347041098 | scikit-learn,0.24.2 |
| 35699 | 135.3586095 | 330.6973161697 | 0.6151303347041098 | numpy,1.19.5 |
| 35699 | 135.3586095 | 330.6973161697 | 0.6151303347041098 | scikit-learn,0.23.2 |
| 35699 | 135.6187154 | 327.0660800934 | 0.6151303347041098 | numpy,1.19.5 |
| 35699 | 135.6187154 | 327.0660800934 | 0.6151303347041098 | scikit-learn,0.22.1 |
| 35699 | 136.49238920000002 | 327.0625619888 | 0.6151303347041098 | numpy,1.19.5 |
| 35699 | 136.49238920000002 | 327.0625619888 | 0.6151303347041098 | scikit-learn,0.22 |
| 35699 | 134.9884524 | 326.9838027954 | 0.6151303347041098 | numpy,1.19.5 |
| 35699 | 134.9884524 | 326.9838027954 | 0.6151303347041098 | scikit-learn,0.21.3 |
| 35699 | 135.30256939999998 | 327.3532209396 | 0.6067516258908594 | numpy,1.19.5 |
| 35699 | 135.30256939999998 | 327.3532209396 | 0.6067516258908594 | scikit-learn,0.20.3 |
| 35699 | 134.9595773 | 326.1311235428 | 0.6067516258908594 | numpy,1.19.5 |
| 35699 | 134.9595773 | 326.1311235428 | 0.6067516258908594 | scikit-learn,0.19.2 |
