Run
10595170

Run 10595170

Task 59 (Supervised Classification) iris Uploaded 26-11-2024 by Serdar Sahin
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Flow

sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler ,classifier=sklearn.ensemble._forest.RandomForestClassifier)(1)A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with a final :term:`predictor` for predictive modeling. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final :term:`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`. For an example use case of `Pipeline` combined with :class:`~s...
sklearn.ensemble._forest.RandomForestClassifier(47)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(47)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(47)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(47)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(47)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(47)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(47)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(47)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(47)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(47)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(47)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(47)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(47)_monotonic_cstnull
sklearn.ensemble._forest.RandomForestClassifier(47)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(47)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(47)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(47)_random_state42
sklearn.ensemble._forest.RandomForestClassifier(47)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(47)_warm_startfalse
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbosefalse
sklearn.preprocessing._data.StandardScaler(20)_copytrue
sklearn.preprocessing._data.StandardScaler(20)_with_meantrue
sklearn.preprocessing._data.StandardScaler(20)_with_stdtrue

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

18 Evaluation measures

Area under ROC curve
0.996 ± 0.0072
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.96 ± 0.048
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.94 ± 0.0699
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9298 ± 0.0559
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0367 ± 0.0268
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4444
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.96 ± 0.0466
Cross-validation details (10-fold Crossvalidation)
Number of instances
150
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.96 ± 0.0355
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.96 ± 0.0466
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.585
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0826 ± 0.0603
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4714
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1458 ± 0.086
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3092 ± 0.1825
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.96 ± 0.0466
Cross-validation details (10-fold Crossvalidation)