Random forest classification

We want to classify the Iris dataset using a Random Forest classifier.

Import

from __future__ import annotations

from gemseo.api import configure_logger
from gemseo.api import load_dataset
from gemseo.mlearning.api import create_classification_model
from numpy import array

configure_logger()
<RootLogger root (INFO)>

Load Iris dataset

iris = load_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("RandomForestClassifier", data=iris)
model.learn()
print(model)
RandomForestClassifier(n_estimators=100)
   based on the scikit-learn library
   built from 150 learning samples

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)
print(output_value)
{'specy': array([0])}

Total running time of the script: ( 0 minutes 0.141 seconds)

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