Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modeling

• URL: http://arxiv.org/abs/2305.18432v1
• Date: Sun, 28 May 2023 23:44:15 GMT
• Title: Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modeling
• Authors: Boris Kovalerchuk Andrew Dunn, Alex Worland, Sridevi Wagle
• Abstract summary: Decision Trees (DTs) are essential in machine learning (ML) because they are used to understand many black box ML models. Two new methods for creation and enhancement with complete visualizing Decision Trees are suggested.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To increase the interpretability and prediction accuracy of the Machine Learning (ML) models, visualization of ML models is a key part of the ML process. Decision Trees (DTs) are essential in machine learning (ML) because they are used to understand many black box ML models including Deep Learning models. In this research, two new methods for creation and enhancement with complete visualizing Decision Trees as understandable models are suggested. These methods use two versions of General Line Coordinates (GLC): Bended Coordinates (BC) and Shifted Paired Coordinates (SPC). The Bended Coordinates are a set of line coordinates, where each coordinate is bended in a threshold point of the respective DT node. In SPC, each n-D point is visualized in a set of shifted pairs of 2-D Cartesian coordinates as a directed graph. These new methods expand and complement the capabilities of existing methods to visualize DT models more completely. These capabilities allow us to observe and analyze: (1) relations between attributes, (2) individual cases relative to the DT structure, (3) data flow in the DT, (4) sensitivity of each split threshold in the DT nodes, and (5) density of cases in parts of the n-D space. These features are critical for DT models' performance evaluation and improvement by domain experts and end users as they help to prevent overgeneralization and overfitting of the models. The advantages of this methodology are illustrated in the case studies on benchmark real-world datasets. The paper also demonstrates how to generalize them for decision tree visualizations in different General Line Coordinates.

Related papers

• General Line Coordinates in 3D [2.9465623430708905]
  Interpretable interactive visual pattern discovery in 3D visualization is a promising way to advance machine learning. It is conducted in 3D General Line Coordinates (GLC) visualization space, which preserves all n-D information in 3D.
  arXiv  Detail & Related papers  (2024-03-17T17:42:20Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Polyp Segmentation [52.06525450636897]
  Automatic polyp segmentation plays a crucial role in the early diagnosis and treatment of colorectal cancer. Existing methods rely heavily on fully supervised training, which requires a large amount of labeled data with time-consuming pixel-wise annotations. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised polyp (DEC-Seg) from colonoscopy images.
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Full High-Dimensional Intelligible Learning In 2-D Lossless Visualization Space [7.005458308454871]
  This study explores a new methodology for machine learning classification tasks in 2-D visualization space (2-D ML) It is shown that this is a full machine learning approach that does not require processing n-dimensional data in an abstract n-dimensional space. It enables discovering n-D patterns in 2-D space without loss of n-D information using graph representations of n-D data in 2-D.
  arXiv  Detail & Related papers  (2023-05-29T00:21:56Z)
• Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks [54.225220638606814]
  We propose a pseudometric for attributed graphs, the Tree Mover's Distance (TMD), and study its relation to generalization. First, we show that TMD captures properties relevant to graph classification; a simple TMD-SVM performs competitively with standard GNNs. Second, we relate TMD to generalization of GNNs under distribution shifts, and show that it correlates well with performance drop under such shifts.
  arXiv  Detail & Related papers  (2022-10-04T21:03:52Z)
• Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models [5.276232626689567]
  This paper proposes a new method SPC-DT to visualize the Decision Tree (DT) as interpretable models. In SPC, each n-D point is visualized in a set of shifted pairs of 2-D Cartesian coordinates as a directed graph. It shows: (1) relations between attributes, (2) individual cases relative to the DT structure, (3) data flow in the DT, (4) how tight each split is to thresholds in the DT nodes, and (5) the density of cases in parts of the n-D space.
  arXiv  Detail & Related papers  (2022-05-09T04:49:29Z)
• Contrastive Neighborhood Alignment [81.65103777329874]
  We present Contrastive Neighborhood Alignment (CNA), a manifold learning approach to maintain the topology of learned features. The target model aims to mimic the local structure of the source representation space using a contrastive loss. CNA is illustrated in three scenarios: manifold learning, where the model maintains the local topology of the original data in a dimension-reduced space; model distillation, where a small student model is trained to mimic a larger teacher; and legacy model update, where an older model is replaced by a more powerful one.
  arXiv  Detail & Related papers  (2022-01-06T04:58:31Z)
• NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go [109.88509362837475]
  We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes. NeuroMorph produces smooth and point-to-point correspondences between them. It works well for a large variety of input shapes, including non-isometric pairs from different object categories.
  arXiv  Detail & Related papers  (2021-06-17T12:25:44Z)
• Full interpretable machine learning in 2D with inline coordinates [9.13755431537592]
  It is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space. It allows discovering n-D patterns in 2-D space without loss of n-D information using graph representations of n-D data in 2-D. The classification and regression algorithms based on these inline coordinates were introduced.
  arXiv  Detail & Related papers  (2021-06-14T16:21:06Z)
• Deep Reinforcement Learning of Graph Matching [63.469961545293756]
  Graph matching (GM) under node and pairwise constraints has been a building block in areas from optimization to computer vision. We present a reinforcement learning solver for GM i.e. RGM that seeks the node correspondence between pairwise graphs. Our method differs from the previous deep graph matching model in the sense that they are focused on the front-end feature extraction and affinity function learning.
  arXiv  Detail & Related papers  (2020-12-16T13:48:48Z)
• Mix Dimension in Poincar\'{e} Geometry for 3D Skeleton-based Action Recognition [57.98278794950759]
  Graph Convolutional Networks (GCNs) have already demonstrated their powerful ability to model the irregular data. We present a novel spatial-temporal GCN architecture which is defined via the Poincar'e geometry. We evaluate our method on two current largest scale 3D datasets.
  arXiv  Detail & Related papers  (2020-07-30T18:23:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.