<jupyter_start><jupyter_text>The _scikit-learn_ Iris example<jupyter_code>from sklearn.datasets import load_iris
iris_dataset = load_iris()
print("Schluessel von iris_dataset: \n{}".format(iris_dataset.keys()))
print(iris_dataset.get("target_names"))

# Macht das gleiche:
print(iris_dataset['target_names'])

print(iris_dataset['feature_names'])
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    iris_dataset['data'], iris_dataset['target'], random_state=0 )
print(X_train.size)
print(X_test.size)<jupyter_output>448
152
{'fraction_non_alphanumeric': 0.12371134020618557, 'fraction_numerical': 0.012027491408934709, 'mean_word_length': 9.796296296296296, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '585925', 'license': 'MIT', 'n_tokens_mistral': 224, 'n_tokens_neox': 217, 'n_words': 34, 'path': 'Python/IrisExample.ipynb', 'repo_name': 'roskenet/Playground', 'size': '582'}