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Uploaded 01-06-2022 by
Sergey Redyuk
sklearn==0.24.2
numpy>=1.6.1
scipy>=0.9
58 runs
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This class implements the regression task.
Parameters
dask_client default: null delete_tmp_folder_after_terminate default: true disable_evaluator_output default: false ensemble_nbest default: 50 ensemble_size default: 50 exclude default: null get_smac_object_callback default: null get_trials_callback default: null include default: null initial_configurations_via_metalearning default: 25 load_models default: true logging_config default: null max_models_on_disc default: 50 memory_limit default: 10000 metadata_directory default: null metric default: null n_jobs default: 1 per_run_time_limit default: 30 resampling_strategy default: "holdout" resampling_strategy_arguments default: null scoring_functions default: null seed default: 1 smac_scenario_args default: null time_left_for_this_task default: 300 tmp_folder default: null
0
Runs
Parameter:
none
dask client
delete tmp folder after terminate
disable evaluator output
ensemble nbest
ensemble size
exclude
get smac object callback
get trials callback
include
initial configurations via metalearning
load models
logging config
max models on disc
memory limit
metadata directory
metric
n jobs
per run time limit
resampling strategy
resampling strategy arguments
scoring functions
seed
smac scenario args
time left for this task
tmp folder
Supervised Classification
Supervised Regression
Learning Curve
Supervised Data Stream Classification
Clustering
Machine Learning Challenge
Survival Analysis
Subgroup Discovery
area under roc curve
average cost
binominal test
build cpu time
build memory
c index
chi-squared
class complexity
class complexity gain
confusion matrix
correlation coefficient
cortana quality
coverage
f measure
information gain
jaccard
kappa
kb relative information score
kohavi wolpert bias squared
kohavi wolpert error
kohavi wolpert sigma squared
kohavi wolpert variance
kononenko bratko information score
matthews correlation coefficient
mean absolute error
mean class complexity
mean class complexity gain
mean f measure
mean kononenko bratko information score
mean precision
mean prior absolute error
mean prior class complexity
mean recall
mean weighted area under roc curve
mean weighted f measure
mean weighted precision
weighted recall
number of instances
os information
positives
precision
predictive accuracy
prior class complexity
prior entropy
probability
quality
ram hours
recall
relative absolute error
root mean prior squared error
root mean squared error
root relative squared error
run cpu time
run memory
run virtual memory
scimark benchmark
single point area under roc curve
total cost
unclassified instance count
usercpu time millis
usercpu time millis testing
usercpu time millis training
webb bias
webb error
webb variance
joint entropy
pattern team auroc10
wall clock time millis
wall clock time millis training
wall clock time millis testing
unweighted recall