<jupyter_start><jupyter_text>***NOTE:*** Actually our algorithms is scoring wellThere are better and simple ways to evaluate classification algorithmswhich we are going to see on upcoming articles<jupyter_code># Visualize the Decision Tree
# Code from http://scikit-learn.org/stable/modules/tree.html
import pydotplus 
dot_data = tree.export_graphviz(clf, out_file=None) 
graph = pydotplus.graph_from_dot_data(dot_data) 
graph.write_pdf("iris.pdf") 
from IPython.display import Image  
dot_data = tree.export_graphviz(clf, out_file=None, 
                         feature_names=iris.feature_names,  
                         class_names=iris.target_names,  
                         filled=True, rounded=True,  
                         special_characters=True)  
graph = pydotplus.graph_from_dot_data(dot_data)  
Image(graph.create_png())<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08285385500575373, 'fraction_numerical': 0.0, 'mean_word_length': 3.484536082474227, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '165181', 'license': 'MIT', 'n_tokens_mistral': 274, 'n_tokens_neox': 263, 'n_words': 54, 'path': 'decision trees/Decision Tree.ipynb', 'repo_name': 'davidfumo/intro-to-machine-learning', 'size': '869'}