<jupyter_start><jupyter_text>https://scikit-learn.org/stable/tutorial/basic/tutorial.htmlconventions<jupyter_code>import numpy as np
from sklearn import random_projection
rng = np.random.RandomState(0)
X = rng.rand(10,2000)
X = np.array(X, dtype='float32')
X.dtype
transformer = random_projection.GaussianRandomProjection()
X_new = transformer.fit_transform(X)
X_new.dtype
from sklearn import datasets
from sklearn.svm import SVC
iris = datasets.load_iris()
clf = SVC(gamma='auto')
clf.fit(iris.data, iris.target)
list(clf.predict(iris.data[:3]))
clf.fit(iris.data, iris.target_names[iris.target])
list(clf.predict(iris.data[:3]))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1393939393939394, 'fraction_numerical': 0.016666666666666666, 'mean_word_length': 7.697368421052632, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, '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': '243167', 'license': 'MIT', 'n_tokens_mistral': 264, 'n_tokens_neox': 256, 'n_words': 39, 'path': 'scikit-Conventions.ipynb', 'repo_name': 'rwwinfree/MLIntro-scikit-learn', 'size': '660'}