Classifier
SummaryClassifier
Summary statistic classifier.
This classifier simply transforms the input data using the SummaryTransformer transformer and builds a provided estimator using the transformed data.
Quickstart
python
from sktime.classification.feature_based import SummaryClassifier
estimator = SummaryClassifier(summary_functions=('mean', 'std', 'min', 'max'), summary_quantiles=(0.25, 0.5, 0.75), estimator=None, n_jobs=1, random_state=None)Tags
Capabilities
- Multivariate
- Probabilistic prediction
- Multiple outputs: Not supported
- Unequal-length series: Not supported
- Missing values: Not supported
- Feature importance: Not supported
- Training-set estimate: Not supported
- Time-limited training: Not supported
Parameters(5)
- summary_functionsstr, list, tuple, default=(“mean”, “std”, “min”, “max”)
- Either a string, or list or tuple of strings indicating the pandas summary functions that are used to summarize each column of the dataset. Must be one of (“mean”, “min”, “max”, “median”, “sum”, “skew”, “kurt”, “var”, “std”, “mad”, “sem”, “nunique”, “count”).
- summary_quantilesstr, list, tuple or None, default=(0.25, 0.5, 0.75)
- Optional list of series quantiles to calculate. If None, no quantiles are calculated.
- estimatorsklearn classifier, default=None
- An sklearn estimator to be built using the transformed data. Defaults to a Random Forest with 200 trees.
- n_jobsint, default=1
The number of jobs to run in parallel for both
fitandpredict.-1means using all processors.- random_stateint or None, default=None
- Seed for random, integer.
Examples
>>> from sktime.classification.feature_based import SummaryClassifier
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (split = "train", return_X_y = True)
>>> X_test, y_test = load_unit_test (split = "test", return_X_y = True)
>>> clf = SummaryClassifier (estimator = RandomForestClassifier (n_estimators = 5))
>>> clf. fit (X_train, y_train) SummaryClassifier(
... )
>>> y_pred = clf. predict (X_test)