Making decision trees accurate again: explaining what explainable AI did not
By Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah Adel Bargal and Joseph E. Gonzalez
The interpretability of neural networks is becoming increasingly necessary, as deep learning is being adopted in settings where accurate and justifiable predictions are required. These applications range from finance to medical imaging. However, deep neural networks are notorious for a lack of justification. Explainable AI (XAI) attempts to bridge this divide between accuracy and interpretability, but as we explain below, XAI justifies decisions without interpreting the model directly.
What is “interpretable”?
Defining explainability or interpretability for computer vision is challenging: What does it even mean to explain a classification for high-dimensional inputs like images? As we discuss below, two popular definitions involve saliency maps and decision trees, but both approaches have their weaknesses.
What explainable AI doesn’t explain
Many XAI methods produce saliency maps, but saliency maps focus on the input and neglect to explain how the model makes decisions. For more on saliency maps, see these saliency tutorials and Github repositories.
Picturing the original image (top), saliency map using a method called Grad-CAM (middle), and another using Guided Backpropagation (bottom). The picture above is the canonical example for “class-discrimination”. The above saliency maps are taken from https://github.com/kazuto1011/grad-cam-pytorch.
What saliency maps fail to explain
To illustrate why saliency maps do not fully explain how the model predicts, here is an example: Below, the saliency maps are identical, but the predictions differ. Why? Even though both saliency maps highlight the correct object, one prediction is incorrect. How? Answering this could help us improve the model, but as shown below, saliency maps fail to explain the model’s decision process.
(Top) The model predicts Eared Grebe. (Bottom) The model predicts Horned Grebe. These are Grad-CAM results for a ResNet18 model trained on Caltech-UCSD Birds-200–2011, or CUB 2011 for short. Although the saliency maps look extremely similar, the model predictions differ. As a result, saliency maps do not explain how the model reached its final prediction.
Another approach is to replace neural networks with interpretable models. Before deep learning, decision trees were the gold standard for accuracy and interpretability. Below, we illustrate the interpretability of decision trees.
Instead of only predicting “Super Burger” or “Waffle fries”, the above decision tree will output a sequence of decisions that lead up to a final prediction. These intermediate decisions can then be verified or challenged separately.
For accuracy, however, decision trees lag behind neural networks by up to 40% accuracy on image classification datasets. Neural-network-and-decision-tree hybrids also underperform, failing to match neural networks on even the dataset CIFAR10, which features tiny 32×32 images like the one below.
As we show in our paper (Sec 5.2), this accuracy gap damages interpretability: high-accuracy, interpretable models are needed to explain high-accuracy neural networks.
Enter neural-backed decision trees
We challenge this false dichotomy by building models that are both interpretable and accurate. Our key insight is to combine neural networks with decision trees, preserving high-level interpretability while using neural networks for low-level decisions, as shown below. We call these models Neural-Backed Decision Trees (NBDTs) and show they can match neural network accuracy while preserving the interpretability of a decision tree.
In this figure, each node contains a neural network. The figure only highlights one such node and the neural network inside. In a neural-backed decision tree, predictions are made via a decision tree, preserving high-level interpretability. However, each node in decision tree is a neural network making low-level decisions. The “low-level” decision made by the neural network above is “Has sausage” or “no sausage”.
NBDTs are as interpretable as decision trees. Unlike neural networks today, NBDTs can output intermediate decisions for a prediction. For example, given an image, a neural network may output Dog. However, an NBDT can output both Dog and Animal, Chordate, Carnivore (below).
Instead of outputting only a prediction “Dog”, Neural-Backed Decision Trees (NBDT) output a series of decisions that lead up to the prediction. Pictured above, the demo NBDT outputs “Animal”, “Chordate”, “Carnivore”, and then “Dog”. The trajectory through the hierarchy is also visualized, to illustrate which possibilities were rejected. The photos above are taken from pexels.com, under the Pexels License.
NBDTs achieve neural network accuracy. Unlike any other decision-tree-based method, NBDTs match neural network accuracy (< 1% difference) on CIFAR10, CIFAR100, and TinyImageNet200. NBDTs also achieve accuracy within 2% of neural networks on ImageNet, setting a new state-of-the-art accuracy for interpretable models. The NBDT’s ImageNet accuracy of 75.30% outperforms the best competing decision-tree-based method by a whole ~14%.
How and what neural-backed decision trees explain
Justifications for individual predictions
The most insightful justifications are for objects the model has never seen before. For example, consider an NBDT (below), and run inference on a Zebra. Although this model has never seen Zebra, the intermediate decisions shown below are correct — Zebras are both Animals and Ungulates (hoofed animal). The ability to see justification for individual predictions is quintessential for unseen objects.
NBDTs make accurate intermediate decisions even for unseen objects. Here, the model was trained on CIFAR10 and has never seen zebras before. Despite that, the NBDT correctly identifies the Zebra as both an Animal and an Ungulate (hoofed animal). The photos above are taken from pexels.com, under the Pexels License.
Justifications for model behavior
Furthermore, we find that with NBDTs, interpretability improves with accuracy. This is contrary to the dichotomy in the introduction: NBDTs not only have both accuracy and interpretability; they also make both accuracy and interpretability the same objective.
The ResNet10 hierarchy (left) makes less sense than the WideResNet hierarchy (right). In this hierarchy, Cat, Frog, and Airplane are placed under the same subtree. The WideResNet hierarchy (right) makes more sense than the ResNet10 hierarchy (left). The WideResNet hierarchy cleanly splits Animals and Vehicles, on each side of the hierarchy.
For example, ResNet10 achieves 4% lower accuracy than WideResNet28x10 on CIFAR10. Correspondingly, the lower-accuracy ResNet^6 hierarchy (left) makes less sense, grouping Frog, Cat, and Airplane together. This is “less sensible,” as it is difficult to find an obvious visual feature shared by all three classes. By contrast, the higher-accuracy WideResNet hierarchy (right) makes more sense, cleanly separating Animal from Vehicle—thus, the higher accuracy, the more interpretable the NBDT.
Understanding decision rules
With low-dimensional tabular data, decision rules in a decision tree are simple to interpret e.g., if the dish contains a bun, then pick the right child, as shown below. However, decision rules are not as straightforward for inputs like high-dimensional images. As we qualitatively find in the paper (Sec 5.3), the model’s decision rules are based not only on object type but also on context, shape, and color.
This example demonstrates how decision rules are easy to interpret with low-dimensional, tabular data. To the right is example tabular data for several items. To the left is a decision tree we trained on this data. In this case, the decision rule (blue) is “Has bun or not?” All items with a bun (orange) are sent to the top child, and all items without a bun (green) are sent to the bottom child.
To interpret decision rules quantitatively, we leverage an existing hierarchy of nouns called WordNet; with this hierarchy, we can find the most specific shared meaning between classes. For example, given the classes Cat and Dog, WordNet would provide Mammal. In our paper (Sec 5.2) and pictured below, we quantitatively verify these WordNet hypotheses.
The WordNet hypothesis for the left subtree (red arrow) is Vehicle. The WordNet hypothesis for the right (blue arrow) is Animal. To validate these meanings qualitatively, we tested the NBDT against unseen classes of objects: 1. Find images that were not seen during training. 2. Given the hypothesis, determine which child each image belongs to. For example, we know that Elephant is an Animal so is *supposed* to go the right subtree. 3. We can now evaluate the hypothesis, by checking how many images are passed to the correct child. For example, check how many Elephant images are sent to the Animal subtree. These accuracies per-class are shown to the right, with unseen Animals (blue) and unseen Vehicles (red) both showing high accuracies.
Note that in small datasets with 10 classes i.e., CIFAR10, we can find WordNet hypotheses for all nodes. However, in large datasets with 1000 classes i.e., ImageNet, we can only find WordNet hypotheses for a subset of nodes.
How it works
The training and inference process for a Neural-Backed Decision Tree can be broken down into four steps.
Training an NBDT occurs in 2 phases: First, construct the hierarchy for the decision tree. Second, train the neural network with a special loss term. To run inference, pass the sample through the neural network backbone. Finally, run the final fully-connected layer as a sequence of decision rules.
- Construct a hierarchy for the decision tree, called the Induced
- This hierarchy yields a particular loss function, which we call the Tree
- Start inference by passing the sample through the neural network backbone.
The backbone is all neural network layers before the final fully-connected
- Finish inference by running the final fully-connected layer as a sequence of
decision rules, which we call Embedded Decision Rules. These decisions
culminate in the final prediction.
Running embedded decision rules
We first discuss inference. As explained above, our NBDT approach featurizes each sample using the neural network backbone. To understand what happens next, we will first construct a degenerate decision tree that is equivalent to a fully-connected layer.
Fully-Connected Layer: Running inference with a featurized sample is a matrix-vector product, as shown below.
This yields a matrix-vector product yields a vector of inner products, which we denote with . The index of the largest inner product is our class prediction.
Naive Decision Tree: We construct a basic decision tree with one root node and a leaf for each class. This is pictured by “B – Naive” in the figure above. Each leaf is directly connected to the root and has a representative vector, namely a row vector from (Eqn. 1 above).
Also pictured above, running inference with a featurized sample means taking inner products between and each child node’s representative vector. Like the fully-connected layer, the index of the largest inner product is our class prediction.
The direct equivalence between a fully-connected layer and a naive decision tree motivates our particular inference method, using an inner-product decision tree. In our work, we then extend this naive tree to deeper trees. However, that discussion is beyond the scope of this article. Our paper (Sec. 3.1) discusses how this works, in detail.
Building induced hierarchies
This hierarchy determines which sets of classes the NBDT must decide between. We refer to this hierarchy as an Induced Hierarchy because we build the hierarchy using a pretrained neural network’s weights.
In particular, we view each row vector in the fully-connected layer’s weight matrix W as a point in d-dimensional space. This is illustrated by “Step B – Set Leaf Vectors“. We then perform hierarchical agglomerative clustering on these points. The successive clustering then determines the hierarchy, as illustrated above. Our paper (Sec. 3.2) discusses this in more detail.
Training with tree supervision loss
Consider “A – Hard” in the figure above. Say the green node corresponds to the Horse class. This is just one class. However, it is also an Animal (orange). As a result, we know that a sample arriving at the root node (blue) should go to the right, to Animal. The sample arriving at the node Animal also should go to the right again, towards Horse. We train each node to predict the correct child node. We call the loss that enforces this the Tree Supervision Loss, which is effectively a cross entropy loss for each node.
Our paper (Sec. 3.3) discusses this in more detail and further explains “B – Soft”.
Trying NBDTs in under a minute
Interested in trying out an NBDT, now? Without installing anything, you can view more example outputs online and even try out our web demo. Alternatively, use our
command-line utility to run inference (Install with
pip install nbdt). Below, we run inference on a picture of a cat.
This outputs both the class prediction and all the intermediate decisions.
Prediction: cat // Decisions: animal (99.47%), chordate (99.20%), carnivore (99.42%), cat (99.86%)
You can load a pretrained NBDT in just a few lines of Python as well. Use the following to get started. We support several WideResNet28x10, ResNet18 for CIFAR100, CIFAR100, and TinyImageNet200.
from nbdt.model import HardNBDT from nbdt.models import wrn28_10_cifar10 model = wrn28_10_cifar10() model = HardNBDT( pretrained=True, dataset='CIFAR10', arch='wrn28_10_cifar10', model=model)
For more instructions on getting started and examples, see our Github repository.
Explainable AI does not fully explain how the neural network reaches a prediction: Existing methods explain the image’s impact on model predictions but do not explain the decision process. Decision trees address this, but unfortunately, images are kryptonite for decision tree accuracy.
We thus combine neural networks and decision trees. Unlike predecessors that arrived at the same hybrid design, our neural-backed decision trees (NBDTs) simultaneously address the failures (1) of neural networks to provide justification and (2) of decision trees to attain high accuracy. This primes a new category of accurate, interpretable NBDTs for applications like medicine and finance. To get started, see the project page.
 There are two types of saliency maps: one is white-box, where the method has access to the model and its parameters. One popular white-box method is Grad-CAM, which uses both gradients and class activation maps to visualize attention. You can learn more from the paper, “Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization”. The other type of saliency map is black-box, where the model does not have access to the model parameters. RISE is one such saliency method. RISE masks random portions of the input image and passes this image through the model — the mask that damages accuracy the most is the most “important” portion. You can learn more from the paper “RISE: Randomized Input Sampling for Explanation of Black-box Models”
 This 40% gap between decision tree and neural network accuracy shows up on TinyImageNet200.
 In general, decision trees perform best with low-dimensional data. Images are the antithesis of this best-case scenario, being extremely high-dimensional.
This article was initially published on the BAIR blog, and appears here with the authors’ permission.