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matbench_v0.1: AMMExpress v2020

Algorithm description:

Automatminer express v1.03.20200727. Based on automatic featurization, tree-based feature reduction, and genetic-algorithm based AutoML with the TPOT package.

Notes:

All data was generated using the same config (express, default). The automatminer version requirement specifies the versions of many dependent packages, such as matminer, which are required for the algorithm to work in your virtualenv.

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

References (in bibtex format):

('@article{Dunn2020,\n'
 '  doi = {10.1038/s41524-020-00406-3},\n'
 '  url = {https://doi.org/10.1038/s41524-020-00406-3},\n'
 '  year = {2020},\n'
 '  month = sep,\n'
 '  publisher = {Springer Science and Business Media {LLC}},\n'
 '  volume = {6},\n'
 '  number = {1},\n'
 '  author = {Alexander Dunn and Qi Wang and Alex Ganose and Daniel Dopp and '
 'Anubhav Jain},\n'
 '  title = {Benchmarking materials property prediction methods: the Matbench '
 'test set and Automatminer reference algorithm},\n'
 '  journal = {npj Computational Materials}\n'
 '}')

User metadata:

{'autofeaturizer_kwargs': {'n_jobs': 10, 'preset': 'express'},
 'cleaner_kwargs': {'feature_na_method': 'drop',
                    'max_na_frac': 0.1,
                    'na_method_fit': 'mean',
                    'na_method_transform': 'mean'},
 'learner_kwargs': {'max_eval_time_mins': 20,
                    'max_time_mins': 1440,
                    'memory': 'auto',
                    'n_jobs': 10,
                    'population_size': 200},
 'learner_name': 'TPOTAdaptor',
 'reducer_kwargs': {'reducers': ['corr', 'tree'],
                    'tree_importance_percentile': 0.99}}

Metadata:

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

Software Requirements

{'python': ['automatminer==1.0.3.20200727', 'matbench==0.1.0']}

Task data:

matbench_dielectric

Fold scores
fold mae rmse mape* max_error
fold_0 0.2188 0.6855 0.0760 14.6654
fold_1 0.2844 1.0764 0.0899 19.6283
fold_2 0.4257 2.9472 0.0889 59.0112
fold_3 0.3198 2.2782 0.0720 53.5196
fold_4 0.3264 1.6137 0.0987 28.1601
Fold score stats
metric mean max min std
mae 0.3150 0.4257 0.2188 0.0672
rmse 1.7202 2.9472 0.6855 0.8140
mape* 0.0851 0.0987 0.0720 0.0098
max_error 34.9969 59.0112 14.6654 17.9782
Fold parameters
fold params dict
fold_0 {'best_pipeline': ['(selectfwe, SelectFwe(alpha=0.006, score_func=<function f_regression at 0x2aaaef1a0840>))', '(robustscaler, RobustScaler(copy=true, quantile_range=(25.0, 75.0), with_centering=true...
fold_1 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.0001))', '(zerocount, ZeroCount())', '(gradientboostingregressor, GradientBoostingRegressor(alpha=0.75, criterion=friedman_mse, in...
fold_2 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.001))', '(minmaxscaler, MinMaxScaler(copy=true, feature_range=(0, 1)))', '(gradientboostingregressor, GradientBoostingRegressor(al...
fold_3 {'best_pipeline': ['(selectfwe, SelectFwe(alpha=0.023, score_func=<function f_regression at 0x2aaaef19f950>))', '(standardscaler, StandardScaler(copy=true, with_mean=true, with_std=true))', '(gradient...
fold_4 {'best_pipeline': ['(selectfwe, SelectFwe(alpha=0.034, score_func=<function f_regression at 0x2aaaf35a08c8>))', '(zerocount, ZeroCount())', '(gradientboostingregressor, GradientBoostingRegressor(alpha...

matbench_expt_gap

Fold scores
fold mae rmse mape* max_error
fold_0 0.3998 0.9435 0.3372 8.0111
fold_1 0.4061 0.9354 0.3085 8.6887
fold_2 0.4538 1.0955 0.3916 12.7533
fold_3 0.4061 1.0273 0.3019 12.6296
fold_4 0.4150 0.9573 0.4503 6.0779
Fold score stats
metric mean max min std
mae 0.4161 0.4538 0.3998 0.0194
rmse 0.9918 1.0955 0.9354 0.0612
mape* 0.3579 0.4503 0.3019 0.0560
max_error 9.6321 12.7533 6.0779 2.6411
Fold parameters
fold params dict
fold_0 {'best_pipeline': ['(selectfwe, SelectFwe(alpha=0.035, score_func=<function f_regression at 0x2aaaf35a18c8>))', '(standardscaler, StandardScaler(copy=true, with_mean=true, with_std=true))', '(gradient...
fold_1 {'best_pipeline': ['(selectfwe, SelectFwe(alpha=0.046, score_func=<function f_regression at 0x2aaaef19f8c8>))', '(onehotencoder, OneHotEncoder(categorical_features=[false, false, false, false, false, ...
fold_2 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.0005))', '(robustscaler, RobustScaler(copy=true, quantile_range=(25.0, 75.0), with_centering=true,\n with_scaling=true...
fold_3 {'best_pipeline': ['(selectpercentile, SelectPercentile(percentile=85,\n score_func=<function f_regression at 0x2aaaf39a38c8>))', '(onehotencoder, OneHotEncoder(categorical_features=[f...
fold_4 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.0005))', '(maxabsscaler, MaxAbsScaler(copy=true))', '(randomforestregressor, RandomForestRegressor(bootstrap=false, criterion=mse,...

matbench_expt_is_metal

Fold scores
fold accuracy balanced_accuracy f1 rocauc
fold_0 0.9218 0.9218 0.9205 0.9218
fold_1 0.9157 0.9156 0.9145 0.9156
fold_2 0.9207 0.9207 0.9193 0.9207
fold_3 0.9228 0.9228 0.9223 0.9228
fold_4 0.9238 0.9238 0.9235 0.9238
Fold score stats
metric mean max min std
accuracy 0.9210 0.9238 0.9157 0.0028
balanced_accuracy 0.9209 0.9238 0.9156 0.0028
f1 0.9200 0.9235 0.9145 0.0031
rocauc 0.9209 0.9238 0.9156 0.0028
Fold parameters
fold params dict
fold_0 {'best_pipeline': ['(selectfwe, SelectFwe(alpha=0.009000000000000001,\n score_func=<function f_classif at 0x2aaaf35a16a8>))', '(onehotencoder, OneHotEncoder(categorical_features=[false, false...
fold_1 {'best_pipeline': ['(rfe, RFE(estimator=ExtraTreesClassifier(bootstrap=false, class_weight=null,\n criterion=gini, max_depth=null,\n ...
fold_2 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.001))', '(maxabsscaler, MaxAbsScaler(copy=true))', '(gradientboostingclassifier, GradientBoostingClassifier(criterion=friedman_mse...
fold_3 {'best_pipeline': ['(selectfwe, SelectFwe(alpha=0.03, score_func=<function f_classif at 0x2aaaf35a0730>))', '(maxabsscaler, MaxAbsScaler(copy=true))', '(gradientboostingclassifier, GradientBoostingCla...
fold_4 {'best_pipeline': ['(rfe, RFE(estimator=ExtraTreesClassifier(bootstrap=false, class_weight=null,\n criterion=entropy, max_depth=null,\n ...

matbench_glass

Fold scores
fold accuracy balanced_accuracy f1 rocauc
fold_0 0.8283 0.8441 0.8697 0.8441
fold_1 0.8125 0.8383 0.8548 0.8383
fold_2 0.8574 0.8546 0.8956 0.8546
fold_3 0.9173 0.8742 0.9437 0.8742
fold_4 0.9375 0.8921 0.9579 0.8921
Fold score stats
metric mean max min std
accuracy 0.8706 0.9375 0.8125 0.0490
balanced_accuracy 0.8607 0.8921 0.8383 0.0199
f1 0.9043 0.9579 0.8548 0.0404
rocauc 0.8607 0.8921 0.8383 0.0199
Fold parameters
fold params dict
fold_0 {'best_pipeline': ['(selectfrommodel, SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=false,\n class_weight=null,\n ...
fold_1 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.0001))', '(standardscaler, StandardScaler(copy=true, with_mean=true, with_std=true))', '(extratreesclassifier, ExtraTreesClassifie...
fold_2 {'best_pipeline': ['(selectpercentile, SelectPercentile(percentile=74,\n score_func=<function f_classif at 0x2aaaf35a0730>))', '(onehotencoder, OneHotEncoder(categorical_features=[fals...
fold_3 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.0001))', '(standardscaler, StandardScaler(copy=true, with_mean=true, with_std=true))', '(gradientboostingclassifier, GradientBoost...
fold_4 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.0001))', '(standardscaler, StandardScaler(copy=true, with_mean=true, with_std=true))', '(gradientboostingclassifier, GradientBoost...

matbench_jdft2d

Fold scores
fold mae rmse mape* max_error
fold_0 29.5070 57.7719 18.9726 362.2752
fold_1 44.3036 98.1137 0.3191 551.7742
fold_2 54.4690 164.0162 0.5117 847.0618
fold_3 28.0759 55.8345 0.2371 316.2185
fold_4 42.8931 156.9938 0.5429 1552.9102
Fold score stats
metric mean max min std
mae 39.8497 54.4690 28.0759 9.8835
rmse 106.5460 164.0162 55.8345 46.6251
mape* 4.1167 18.9726 0.2371 7.4289
max_error 726.0480 1552.9102 316.2185 453.6535
Fold parameters
fold params dict
fold_0 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.1))', '(minmaxscaler, MinMaxScaler(copy=true, feature_range=(0, 1)))', '(gradientboostingregressor, GradientBoostingRegressor(alph...
fold_1 {'best_pipeline': ['(selectpercentile, SelectPercentile(percentile=40,\n score_func=<function f_regression at 0x2aaaf35a08c8>))', '(maxabsscaler, MaxAbsScaler(copy=true))', '(gradientb...
fold_2 {'best_pipeline': ['(selectpercentile, SelectPercentile(percentile=62,\n score_func=<function f_regression at 0x2aaaf35a08c8>))', '(onehotencoder, OneHotEncoder(categorical_features=[f...
fold_3 {'best_pipeline': ['(selectpercentile, SelectPercentile(percentile=82,\n score_func=<function f_regression at 0x2aab561f6620>))', '(robustscaler, RobustScaler(copy=true, quantile_range...
fold_4 {'best_pipeline': ['(selectpercentile, SelectPercentile(percentile=62,\n score_func=<function f_regression at 0x2aaaf35a08c8>))', '(zerocount, ZeroCount())', '(gradientboostingregresso...

matbench_log_gvrh

Fold scores
fold mae rmse mape* max_error
fold_0 0.0891 0.1270 0.0692 1.1580
fold_1 0.0852 0.1261 0.0666 1.0887
fold_2 0.0849 0.1261 0.0668 0.9631
fold_3 0.0884 0.1279 0.0670 0.8959
fold_4 0.0894 0.1313 0.0690 0.9810
Fold score stats
metric mean max min std
mae 0.0874 0.0894 0.0849 0.0020
rmse 0.1277 0.1313 0.1261 0.0019
mape* 0.0677 0.0692 0.0666 0.0012
max_error 1.0173 1.1580 0.8959 0.0937
Fold parameters
fold params dict
fold_0 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.2))', '(zerocount, ZeroCount())', '(gradientboostingregressor, GradientBoostingRegressor(alpha=0.99, criterion=friedman_mse, init=...
fold_1 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.01))', '(robustscaler, RobustScaler(copy=true, quantile_range=(25.0, 75.0), with_centering=true,\n with_scaling=true))...
fold_2 {'best_pipeline': ['(selectfwe, SelectFwe(alpha=0.01, score_func=<function f_regression at 0x2aaaef19e8c8>))', '(minmaxscaler, MinMaxScaler(copy=true, feature_range=(0, 1)))', '(randomforestregressor,...
fold_3 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.0001))', '(standardscaler, StandardScaler(copy=true, with_mean=true, with_std=true))', '(gradientboostingregressor, GradientBoosti...
fold_4 {'best_pipeline': ['(selectpercentile, SelectPercentile(percentile=96,\n score_func=<function f_regression at 0x2aaaf35a08c8>))', '(maxabsscaler, MaxAbsScaler(copy=true))', '(extratree...

matbench_log_kvrh

Fold scores
fold mae rmse mape* max_error
fold_0 0.0639 0.1179 0.0417 1.4823
fold_1 0.0659 0.1231 0.0432 1.2686
fold_2 0.0627 0.1115 0.0411 1.1316
fold_3 0.0668 0.1217 0.0464 1.1890
fold_4 0.0640 0.1172 0.0417 1.4335
Fold score stats
metric mean max min std
mae 0.0647 0.0668 0.0627 0.0015
rmse 0.1183 0.1231 0.1115 0.0041
mape* 0.0428 0.0464 0.0411 0.0019
max_error 1.3010 1.4823 1.1316 0.1362
Fold parameters
fold params dict
fold_0 {'best_pipeline': ['(selectfwe, SelectFwe(alpha=0.032, score_func=<function f_regression at 0x2aaaf35a2840>))', '(minmaxscaler, MinMaxScaler(copy=true, feature_range=(0, 1)))', '(extratreesregressor, ...
fold_1 {'best_pipeline': ['(selectfwe, SelectFwe(alpha=0.029, score_func=<function f_regression at 0x2aaaf35a08c8>))', '(zerocount, ZeroCount())', '(gradientboostingregressor, GradientBoostingRegressor(alpha...
fold_2 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.2))', '(onehotencoder, OneHotEncoder(categorical_features=[false, false, false, false, false, false,\n ...
fold_3 {'best_pipeline': ['(selectfwe, SelectFwe(alpha=0.016, score_func=<function f_regression at 0x2aaaf79a28c8>))', '(onehotencoder, OneHotEncoder(categorical_features=[false, false, false, false, false, ...
fold_4 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.0001))', '(minmaxscaler, MinMaxScaler(copy=true, feature_range=(0, 1)))', '(extratreesregressor, ExtraTreesRegressor(bootstrap=fal...

matbench_mp_e_form

Fold scores
fold mae rmse mape* max_error
fold_0 0.1586 0.2508 1.0829 4.0713
fold_1 0.2026 0.2955 0.9253 5.8108
fold_2 0.1473 0.2256 0.7722 2.7696
fold_3 0.2080 0.3062 1.3958 5.5190
fold_4 0.1467 0.2226 0.8028 3.3888
Fold score stats
metric mean max min std
mae 0.1726 0.2080 0.1467 0.0270
rmse 0.2602 0.3062 0.2226 0.0348
mape* 0.9958 1.3958 0.7722 0.2280
max_error 4.3119 5.8108 2.7696 1.1826
Fold parameters
fold params dict
fold_0 {'best_pipeline': ['(gradientboostingregressor, GradientBoostingRegressor(alpha=0.75, criterion=friedman_mse, init=null,\n learning_rate=0.5, loss=huber, max_depth=5,\n max_fea...
fold_1 {'best_pipeline': ['(polynomialfeatures, PolynomialFeatures(degree=2, include_bias=false, interaction_only=false))', '(pca, PCA(copy=true, iterated_power=3, n_components=null, random_state=null,\n sv...
fold_2 {'best_pipeline': ['(stackingestimator, StackingEstimator(estimator=GradientBoostingRegressor(alpha=0.9, criterion=friedman_mse, init=null,\n learning_rate=0.5, loss=huber, max_depth=4,\n ...
fold_3 {'best_pipeline': ['(minmaxscaler, MinMaxScaler(copy=true, feature_range=(0, 1)))', '(selectfwe, SelectFwe(alpha=0.027, score_func=<function f_regression at 0x2b2eb18422f0>))', '(stackingestimator, St...
fold_4 {'best_pipeline': ['(xgbregressor, XGBRegressor(base_score=0.5, booster=gbtree, colsample_bylevel=1,\n colsample_bytree=1, gamma=0, learning_rate=0.5, max_delta_step=0,\n max_depth=5, min_...

matbench_mp_gap

Fold scores
fold mae rmse mape* max_error
fold_0 0.2799 0.5481 3.5712 5.4792
fold_1 0.2850 0.5671 3.1533 6.9105
fold_2 0.2724 0.5477 4.6097 6.2045
fold_3 0.2909 0.5710 10.0191 6.4590
fold_4 0.2837 0.5714 6.8322 5.5333
Fold score stats
metric mean max min std
mae 0.2824 0.2909 0.2724 0.0061
rmse 0.5611 0.5714 0.5477 0.0109
mape* 5.6371 10.0191 3.1533 2.5347
max_error 6.1173 6.9105 5.4792 0.5480
Fold parameters
fold params dict
fold_0 {'best_pipeline': ['(stackingestimator-1, StackingEstimator(estimator=RandomForestRegressor(bootstrap=false, criterion=mse, max_depth=null,\n max_features=0.4, max_leaf_nodes=null,\n ...
fold_1 {'best_pipeline': ['(stackingestimator-1, StackingEstimator(estimator=RandomForestRegressor(bootstrap=true, criterion=mse, max_depth=null,\n max_features=0.35000000000000003, max_leaf_nodes=...
fold_2 {'best_pipeline': ['(stackingestimator-1, StackingEstimator(estimator=RandomForestRegressor(bootstrap=false, criterion=mse, max_depth=null,\n max_features=0.45, max_leaf_nodes=null,\n ...
fold_3 {'best_pipeline': ['(stackingestimator-1, StackingEstimator(estimator=ExtraTreesRegressor(bootstrap=false, criterion=mse, max_depth=null,\n max_features=0.45, max_leaf_nodes=null,\n ...
fold_4 {'best_pipeline': ['(stackingestimator-1, StackingEstimator(estimator=GradientBoostingRegressor(alpha=0.85, criterion=friedman_mse, init=null,\n learning_rate=0.01, loss=lad, max_depth=1,\...

matbench_mp_is_metal

Fold scores
fold accuracy balanced_accuracy f1 rocauc
fold_0 0.9133 0.9094 0.8982 0.9094
fold_1 0.9123 0.9086 0.8972 0.9086
fold_2 0.9129 0.9089 0.8976 0.9089
fold_3 0.9146 0.9108 0.8998 0.9108
fold_4 0.9129 0.9086 0.8974 0.9086
Fold score stats
metric mean max min std
accuracy 0.9132 0.9146 0.9123 0.0008
balanced_accuracy 0.9093 0.9108 0.9086 0.0008
f1 0.8981 0.8998 0.8972 0.0009
rocauc 0.9093 0.9108 0.9086 0.0008
Fold parameters
fold params dict
fold_0 {'best_pipeline': ['(minmaxscaler, MinMaxScaler(copy=true, feature_range=(0, 1)))', '(randomforestclassifier, RandomForestClassifier(bootstrap=false, class_weight=null,\n criterion=entropy,...
fold_1 {'best_pipeline': ['(minmaxscaler, MinMaxScaler(copy=true, feature_range=(0, 1)))', '(randomforestclassifier, RandomForestClassifier(bootstrap=false, class_weight=null,\n criterion=entropy,...
fold_2 {'best_pipeline': ['(minmaxscaler, MinMaxScaler(copy=true, feature_range=(0, 1)))', '(randomforestclassifier, RandomForestClassifier(bootstrap=false, class_weight=null,\n criterion=entropy,...
fold_3 {'best_pipeline': ['(stackingestimator, StackingEstimator(estimator=RandomForestClassifier(bootstrap=false, class_weight=null,\n criterion=entropy, max_depth=null, max_features=0.5,\n ...
fold_4 {'best_pipeline': ['(featureunion, FeatureUnion(n_jobs=null,\n transformer_list=[(functiontransformer, FunctionTransformer(accept_sparse=false, check_inverse=true,\n func=<function copy...

matbench_perovskites

Fold scores
fold mae rmse mape* max_error
fold_0 0.2159 0.3114 0.2077 2.7651
fold_1 0.1904 0.2857 0.1944 2.6783
fold_2 0.1962 0.2869 0.1933 2.4466
fold_3 0.1992 0.2907 0.2209 3.3116
fold_4 0.2006 0.3023 0.1886 2.4386
Fold score stats
metric mean max min std
mae 0.2005 0.2159 0.1904 0.0085
rmse 0.2954 0.3114 0.2857 0.0099
mape* 0.2010 0.2209 0.1886 0.0118
max_error 2.7280 3.3116 2.4386 0.3186
Fold parameters
fold params dict
fold_0 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.1))', '(robustscaler, RobustScaler(copy=true, quantile_range=(25.0, 75.0), with_centering=true,\n with_scaling=true))'...
fold_1 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.1))', '(zerocount, ZeroCount())', '(randomforestregressor, RandomForestRegressor(bootstrap=false, criterion=mse, max_depth=null,\n...
fold_2 {'best_pipeline': ['(selectfwe, SelectFwe(alpha=0.03, score_func=<function f_regression at 0x2aaaf35a08c8>))', '(minmaxscaler, MinMaxScaler(copy=true, feature_range=(0, 1)))', '(gradientboostingregres...
fold_3 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.1))', '(robustscaler, RobustScaler(copy=true, quantile_range=(25.0, 75.0), with_centering=true,\n with_scaling=true))'...
fold_4 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.05))', '(maxabsscaler, MaxAbsScaler(copy=true))', '(randomforestregressor, RandomForestRegressor(bootstrap=false, criterion=mse, m...

matbench_phonons

Fold scores
fold mae rmse mape* max_error
fold_0 67.5727 146.7970 0.1079 1151.5570
fold_1 54.0755 100.2097 0.1048 890.4159
fold_2 50.9853 96.5991 0.0931 680.9361
fold_3 59.6458 127.8555 0.1142 926.0969
fold_4 48.5738 77.0626 0.0958 383.1912
Fold score stats
metric mean max min std
mae 56.1706 67.5727 48.5738 6.7981
rmse 109.7048 146.7970 77.0626 24.6280
mape* 0.1032 0.1142 0.0931 0.0078
max_error 806.4394 1151.5570 383.1912 258.9850
Fold parameters
fold params dict
fold_0 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.01))', '(robustscaler, RobustScaler(copy=true, quantile_range=(25.0, 75.0), with_centering=true,\n with_scaling=true))...
fold_1 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.005))', '(maxabsscaler, MaxAbsScaler(copy=true))', '(gradientboostingregressor, GradientBoostingRegressor(alpha=0.8, criterion=fri...
fold_2 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.1))', '(minmaxscaler, MinMaxScaler(copy=true, feature_range=(0, 1)))', '(extratreesregressor, ExtraTreesRegressor(bootstrap=false,...
fold_3 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.0001))', '(onehotencoder, OneHotEncoder(categorical_features=[false, false, false, false, false, false,\n ...
fold_4 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.2))', '(minmaxscaler, MinMaxScaler(copy=true, feature_range=(0, 1)))', '(extratreesregressor, ExtraTreesRegressor(bootstrap=false,...

matbench_steels

Fold scores
fold mae rmse mape* max_error
fold_0 109.3058 188.8049 0.0693 1082.7703
fold_1 80.4188 109.2771 0.0569 416.3620
fold_2 83.5360 120.2935 0.0607 424.5913
fold_3 98.7186 136.5898 0.0722 473.4563
fold_4 115.4851 215.1149 0.0891 1142.9223
Fold score stats
metric mean max min std
mae 97.4929 115.4851 80.4188 13.7919
rmse 154.0161 215.1149 109.2771 40.9531
mape* 0.0696 0.0891 0.0569 0.0112
max_error 708.0205 1142.9223 416.3620 331.6607
Fold parameters
fold params dict
fold_0 {'best_pipeline': ['(selectfrommodel, SelectFromModel(estimator=ExtraTreesRegressor(bootstrap=false, criterion=mse,\n max_depth=null,\n ...
fold_1 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.1))', '(fastica, FastICA(algorithm=parallel, fun=logcosh, fun_args=null, max_iter=200,\n n_components=null, random_state=nu...
fold_2 {'best_pipeline': ['(selectpercentile, SelectPercentile(percentile=53,\n score_func=<function f_regression at 0x2aaaf79a38c8>))', '(minmaxscaler, MinMaxScaler(copy=true, feature_range=...
fold_3 {'best_pipeline': ['(variancethreshold, VarianceThreshold(threshold=0.1))', '(minmaxscaler, MinMaxScaler(copy=true, feature_range=(0, 1)))', '(kneighborsregressor, KNeighborsRegressor(algorithm=auto, ...
fold_4 {'best_pipeline': ['(selectfrommodel, SelectFromModel(estimator=ExtraTreesRegressor(bootstrap=false, criterion=mse,\n max_depth=null,\n ...