We used color to highlight an important dimension (target category) because humans quickly and easily pick out color differences. The prediction leaves are not very pure because training a model on just a single variable leads to a poor model, but this restricted example demonstrates how decision trees carve up feature space. Once we moved to SVG files, we unnecessarily parsed the SVG files to get the size for use in the HTML; as we wrote this document we realized extracting the size information from SVG files was unnecessary. regr = regr.fit(X_train, y_train), viz = dtreeviz(regr, X_train, y_train, target_name='price', So, we've created a general package for scikit-learn decision tree visualization and model interpretation, which we'll be using heavily in an upcoming machine learning book (written with Jeremy Howard). For example, combining the histograms of nodes 9 and 12 yields the histogram of node 8. A target class color legend would be nice. Unfortunately, that meant we had to specify the actual size we wanted for the overall tree using an HTML table in graphviz using width and height parameters on
| tags. The single biggest headache was convincing all components of our solution to produce high-quality vector graphics. You've just found the most powerful. It took us four hours to figure out that generating and importing SVG were two different things and we needed the following magic incantation on OS X using --with-librsvg: Originally, when we resorted to generating PNG files from matplotlib, we set the dots per inch (dpi) to be 450 so that they looked okay on high resolution screens like the iMac. Here's a regressor decision tree trained using a single feature from the Boston data, AGE, and with node ID labeling turned on for discussion purposes here: Horizontal dashed lines indicate the target mean for the left and right buckets in decision nodes; a vertical dashed line indicates the split point in feature space. We'd like to know how many samples each leaf has, how pure the target values are, and just generally where most of the weight of samples falls. For classifier trees, the prediction is a target category (represented as an integer in scikit), such as cancer or not-cancer. The left bucket has observations whose xi feature values are all less than the split point and the right bucket has observations whose xi is greater than the split point. Now, let's take a look at visualizing how a specific feature vector yields a specific prediction. (is that a kitty cat or lion? In terms of interaction, TreeMap provides a zooming interface as well as the Other than the educational animation in A visual introduction to machine learning, we couldn't find a decision tree visualization package that illustrates how the feature space is split up at decision nodes (feature-target space). The black wedge highlights the split point and identifies the exact split value. Our initial coding experiments led us to create a shadow tree wrapping the decision trees created by scikit, so let's start with that. Before digging into the previous state-of-the-art visualizations, we'd like to give a little spoiler to show what's possible. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. Size, color, height, and labels can be mapped to any attribute. The visual appearance of a treemap is highly configurable. X = testX) The ability to visualize a specific vector run down the tree does not seem to be generally available. The key here is to examine the decisions taken along the path from the root to the leaf predictor node. regr = tree.DecisionTreeRegressor(max_depth=3) For example, the AGE feature space in node 0 is split into the regions of AGE future space shown in nodes 1 and 8. This generally means examining things like tree shape and size, but more importantly, it means looking at the leaves. We use a pie chart for classifier leaves, despite their bad reputation. testX = X_train[5,:] As with the regressor, the feature space of a left child is everything to the left of the parent's split point in the same feature space; similarly for the right child. Copyright © 2006-2020 Macrofocus GmbH - All rights reserved - Swiss Made. Looking for a good TreeMap visualization tool? At this point, we haven't tested the visualizations on anything but OS X. As you can see, each AGE feature axis uses the same range, rather than zooming in, to make it easier to compare decision nodes. Construction stops when some stopping criterion is reached, such as having less than five observations in the node. Training of a decision node chooses feature xi and split value within xi's range of values (feature space) to group samples with similar target values into two buckets. Here's a classifier tree trained on the USER KNOWLEDGE data, again with a single feature (PEG) and with nodes labeled for discussion purposes: Ignoring color, the histogram shows the PEG feature space distribution. SAS visualization (best image quality we could find with numeric features), the decision nodes show how the feature space is split, the split points for decision nodes are shown visually (as a wedge) in the distribution, the leaf size is proportional to the number of samples in that leaf, All colors were handpicked from colorblind safe palettes, one handpicked palette per number of target categories (2 through 10), We use a gray rather than black for text because it's easier on the eyes, We draw outlines of bars in bar charts and slices in pie charts. Some users have a preference for left-to-right orientation instead of top-down and sometimes the nature of the tree simply flows better left-to-right. Macrofocus TreeMap aims at democratizing interactive visualization and is. At the highest level, we used matplotlib to generate images for decision and leaf nodes and combined them into a tree using the venerable graphviz.
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