Note
Click here to download the full example code
Classification API¶
Here are some examples of the machine learning API applied to classification models.
from __future__ import division, unicode_literals
from gemseo.api import configure_logger, load_dataset
from gemseo.mlearning.api import (
create_classification_model,
get_classification_models,
get_classification_options,
)
configure_logger()
Out:
<RootLogger root (INFO)>
Get available classification models¶
print(get_classification_models())
Out:
['KNNClassifier', 'RandomForestClassifier', 'SVMClassifier']
Get classification model options¶
print(get_classification_options("KNNClassifier"))
Out:
{'$schema': 'http://json-schema.org/schema#', 'type': 'object', 'properties': {'transformer': {'type': 'null'}, 'input_names': {'type': 'null'}, 'output_names': {'type': 'null'}, 'n_neighbors': {'description': 'The number of neighbors.', 'type': 'integer'}}, 'required': ['n_neighbors']}
Create classification model¶
iris = load_dataset("IrisDataset", as_io=True)
model = create_classification_model("KNNClassifier", data=iris)
model.learn()
print(model)
Out:
KNNClassifier(n_neighbors=5)
based on the scikit-learn library
built from 150 learning samples
Total running time of the script: ( 0 minutes 0.032 seconds)