17488 10963 sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=xgboost.sklearn.XGBClassifier) sklearn.Pipeline(SimpleImputer,XGBClassifier) sklearn.pipeline.Pipeline 1 openml==0.10.2,sklearn==0.21.2,xgboost==0.90 Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``. 2020-01-10T21:36:36 English sklearn==0.21.2 numpy>=1.6.1 scipy>=0.9 memory None null Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming steps list [{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}] List of (name, transform) tuples (implementing fit/transform) that are chained, in the order in which they are chained, with the last object an estimator verbose boolean false If True, the time elapsed while fitting each step will be printed as it is completed. imputer 17407 1 sklearn.impute._base.SimpleImputer sklearn.SimpleImputer sklearn.impute._base.SimpleImputer 11 openml==0.10.2,sklearn==0.21.2 Imputation transformer for completing missing values. 2019-11-22T01:19:36 English sklearn==0.21.2 numpy>=1.6.1 scipy>=0.9 add_indicator boolean false If True, a `MissingIndicator` transform will stack onto output of the imputer's transform. This allows a predictive estimator to account for missingness despite imputation. If a feature has no missing values at fit/train time, the feature won't appear on the missing indicator even if there are missing values at transform/test time. copy boolean true If True, a copy of X will be created. If False, imputation will be done in-place whenever possible. Note that, in the following cases, a new copy will always be made, even if `copy=False`: - If X is not an array of floating values; - If X is encoded as a CSR matrix; - If add_indicator=True fill_value string or numerical value -1 When strategy == "constant", fill_value is used to replace all occurrences of missing_values If left to the default, fill_value will be 0 when imputing numerical data and "missing_value" for strings or object data types missing_values number NaN The placeholder for the missing values. All occurrences of `missing_values` will be imputed strategy string "constant" The imputation strategy - If "mean", then replace missing values using the mean along each column. Can only be used with numeric data - If "median", then replace missing values using the median along each column. Can only be used with numeric data - If "most_frequent", then replace missing using the most frequent value along each column. Can be used with strings or numeric data - If "constant", then replace missing values with fill_value. Can be used with strings or numeric data .. versionadded:: 0.20 strategy="constant" for fixed value imputation verbose integer 0 Controls the verbosity of the imputer openml-python python scikit-learn sklearn sklearn_0.21.2 estimator 17489 10963 xgboost.sklearn.XGBClassifier xgboost.XGBClassifier xgboost.sklearn.XGBClassifier 8 openml==0.10.2,sklearn==0.21.2,xgboost==0.90 Implementation of the scikit-learn API for XGBoost classification. 2020-01-10T21:36:36 English sklearn==0.21.2 numpy>=1.6.1 scipy>=0.9 base_score 0.5 booster "gbtree" colsample_bylevel float 1 Subsample ratio of columns for each level colsample_bynode float 1 Subsample ratio of columns for each split colsample_bytree float 1 Subsample ratio of columns when constructing each tree gamma float 0 Minimum loss reduction required to make a further partition on a leaf node of the tree learning_rate float 0.1 Boosting learning rate (xgb's "eta") max_delta_step int 0 Maximum delta step we allow each tree's weight estimation to be max_depth int 3 Maximum tree depth for base learners min_child_weight int 1 Minimum sum of instance weight(hessian) needed in a child missing float null Value 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" n_estimators int 100 Number of trees to fit n_jobs int 1 Number of parallel threads used to run xgboost. (replaces ``nthread``) nthread int null Number of parallel threads used to run xgboost. (Deprecated, please use ``n_jobs``) objective string or callable "multi:softprob" Specify 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 dart random_state int 42 Random number seed. (replaces seed) reg_alpha float 0 L1 regularization term on weights reg_lambda float 1 L2 regularization term on weights scale_pos_weight float 1 Balancing of positive and negative weights base_score: The initial prediction score of all instances, global bias seed int null Random number seed. (Deprecated, please use random_state) silent boolean null Whether to print messages while running boosting. Deprecated. Use verbosity instead subsample float 1 Subsample ratio of the training instance verbosity int 1 The degree of verbosity. Valid values are 0 (silent) - 3 (debug) openml-python python scikit-learn sklearn sklearn_0.21.2 openml-python python scikit-learn sklearn sklearn_0.21.2