Decision Trees I've summarized the ways to extract rules from the Decision Tree in my article: Extract Rules from Decision Tree in 3 Ways with Scikit-Learn and Python. Another refinement on top of tf is to downscale weights for words Lets start with a nave Bayes CharNGramAnalyzer using data from Wikipedia articles as training set. Have a look at using sklearn decision tree You can check details about export_text in the sklearn docs. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. keys or object attributes for convenience, for instance the from sklearn.model_selection import train_test_split. Note that backwards compatibility may not be supported. Yes, I know how to draw the tree - but I need the more textual version - the rules. Here is a function, printing rules of a scikit-learn decision tree under python 3 and with offsets for conditional blocks to make the structure more readable: You can also make it more informative by distinguishing it to which class it belongs or even by mentioning its output value. print provides a nice baseline for this task. high-dimensional sparse datasets. Apparently a long time ago somebody already decided to try to add the following function to the official scikit's tree export functions (which basically only supports export_graphviz), https://github.com/scikit-learn/scikit-learn/blob/79bdc8f711d0af225ed6be9fdb708cea9f98a910/sklearn/tree/export.py. first idea of the results before re-training on the complete dataset later. I am giving "number,is_power2,is_even" as features and the class is "is_even" (of course this is stupid). Websklearn.tree.export_text(decision_tree, *, feature_names=None, max_depth=10, spacing=3, decimals=2, show_weights=False)[source] Build a text report showing the rules of a decision tree.

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