Visibility: public Uploaded 21-02-2020 by Nelly Palacios sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 0 runs
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  • openml-python python scikit-learn sklearn sklearn_0.22.1
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Implementation of the scikit-learn API for XGBoost classification.


base_scoredefault: 0.5
boosterdefault: "gbtree"
colsample_bylevelSubsample ratio of columns for each leveldefault: 1
colsample_bynodeSubsample ratio of columns for each splitdefault: 1
colsample_bytreeSubsample ratio of columns when constructing each treedefault: 1
gammaMinimum loss reduction required to make a further partition on a leaf node of the treedefault: 0
learning_rateBoosting learning rate (xgb's "eta")default: 0.1
max_delta_stepMaximum delta step we allow each tree's weight estimation to bedefault: 0
max_depthMaximum tree depth for base learnersdefault: 3
min_child_weightMinimum sum of instance weight(hessian) needed in a childdefault: 1
missingValue in the data which needs to be present as a missing value. If None, defaults to np.nan importance_type: string, default "gain" The feature importance type for the feature_importances_ property: either "gain", "weight", "cover", "total_gain" or "total_cover"default: null
n_estimatorsNumber of trees to fitdefault: 100
n_jobsNumber of parallel threads used to run xgboost. (replaces ``nthread``)default: 1
nthreadNumber of parallel threads used to run xgboost. (Deprecated, please use ``n_jobs``)default: null
objectiveSpecify the learning task and the corresponding learning objective or a custom objective function to be used (see note below) booster: string Specify which booster to use: gbtree, gblinear or dartdefault: "binary:logistic"
random_stateRandom number seed. (replaces seed)default: 0
reg_alphaL1 regularization term on weightsdefault: 0
reg_lambdaL2 regularization term on weightsdefault: 1
scale_pos_weightBalancing of positive and negative weights base_score: The initial prediction score of all instances, global biasdefault: 1
seedRandom number seed. (Deprecated, please use random_state)default: null
silentWhether to print messages while running boosting. Deprecated. Use verbosity insteaddefault: null
subsampleSubsample ratio of the training instancedefault: 1
verbosityThe degree of verbosity. Valid values are 0 (silent) - 3 (debug)default: 1


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