<jupyter_start><jupyter_text>Import Evaluation Metrics<jupyter_code>from sklearn.model_selection import train_test_split
# Read in the data.
data = np.asarray(pd.read_csv('eval-met.csv', header=None))
# Assign the features to the variable X, and the labels to the variable y. 
X = data[:,0:2]
y = data[:,2]

# Use train test split to split your data 
# Use a test size of 25% and a random state of 42
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .25)
# Instantiate your decision tree model
model = DecisionTreeClassifier()

# TODO: Fit the model to the training data.
model.fit(X_train, y_train)
# TODO: Make predictions on the test data
y_pred = model.predict(X_test)<jupyter_output><empty_output><jupyter_text>TODO: Calculate the accuracy and assign it to the variable acc on the test data.<jupyter_code>acc = accuracy_score(y_test, y_pred)
acc*100<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09392265193370165, 'fraction_numerical': 0.013259668508287293, 'mean_word_length': 5.81203007518797, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '258725', 'license': 'MIT', 'n_tokens_mistral': 320, 'n_tokens_neox': 308, 'n_words': 102, 'path': 'Supervised-Learning/Model Evaluation Metrics.ipynb', 'repo_name': 'immu0001/Udacity-Data-Scientist-Nanodegree-TERM-1', 'size': '905'}