Note
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K nearest neighbors classification¶
We want to classify the Iris dataset using a KNN classifier.
from __future__ import annotations
from numpy import array
from gemseo import configure_logger
from gemseo import create_benchmark_dataset
from gemseo.mlearning import create_classification_model
configure_logger()
<RootLogger root (INFO)>
Load Iris dataset¶
iris = create_benchmark_dataset("IrisDataset", as_io=True)
Create the classification model¶
Then, we build the linear regression model from the discipline cache and displays this model.
model = create_classification_model("KNNClassifier", data=iris)
model.learn()
model
Predict output¶
Once it is built, we can use it for prediction.
input_value = {
"sepal_length": array([4.5]),
"sepal_width": array([3.0]),
"petal_length": array([1.0]),
"petal_width": array([0.2]),
}
output_value = model.predict(input_value)
output_value
{'specy': array([0])}
Total running time of the script: (0 minutes 0.035 seconds)