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matbench_v0.1: RF-Regex Steels

Algorithm description:

The RF algorithm from sklearn is used.

Notes:

No special considerations required. Key is to convert the composition string properly into a table

Raw data download and example notebook available on the matbench repo.

References (in bibtex format):

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User metadata:

{}

Metadata:

tasks recorded 1/13
complete?
composition complete?
structure complete?
regression complete?
classification complete?

Software Requirements

{'python': ['scikit-learn==0.24.1', 'numpy==1.20.1', 'matbench==0.1.0']}

Task data:

matbench_steels

Fold scores
fold mae rmse mape* max_error
fold_0 97.5404 135.2950 0.0660 500.0100
fold_1 86.2789 120.2379 0.0620 422.3500
fold_2 79.5099 114.1154 0.0559 357.8433
fold_3 94.5817 128.5511 0.0678 328.0567
fold_4 95.0372 142.2333 0.0720 505.2967
Fold score stats
metric mean max min std
mae 90.5896 97.5404 79.5099 6.7138
rmse 128.0865 142.2333 114.1154 10.0906
mape* 0.0647 0.0720 0.0559 0.0054
max_error 422.7113 505.2967 328.0567 72.0596
Fold parameters
fold params dict
fold_0 {}
fold_1 {}
fold_2 {}
fold_3 {}
fold_4 {}