256 research outputs found
vivid: An R package for Variable Importance and Variable Interactions Displays for Machine Learning Models
We present vivid, an R package for visualizing variable importance and
variable interactions in machine learning models. The package provides a range
of displays including heatmap and graph-based displays for viewing variable
importance and interaction jointly and partial dependence plots in both a
matrix layout and an alternative layout emphasizing important variable subsets.
With the intention of increasing a machine learning models' interpretability
and making the work applicable to a wider readership, we discuss the design
choices behind our implementation by focusing on the package structure and
providing an in-depth look at the package functions and key features. We also
provide a practical illustration of the software in use on a data set.Comment: 15 pages, 7 figure
Dynamic Modulation of Local Population Activity by Rhythm Phase in Human Occipital Cortex During a Visual Search Task
Brain rhythms are more than just passive phenomena in visual cortex. For the first time, we show that the physiology underlying brain rhythms actively suppresses and releases cortical areas on a second-to-second basis during visual processing. Furthermore, their influence is specific at the scale of individual gyri. We quantified the interaction between broadband spectral change and brain rhythms on a second-to-second basis in electrocorticographic (ECoG) measurement of brain surface potentials in five human subjects during a visual search task. Comparison of visual search epochs with a blank screen baseline revealed changes in the raw potential, the amplitude of rhythmic activity, and in the decoupled broadband spectral amplitude. We present new methods to characterize the intensity and preferred phase of coupling between broadband power and band-limited rhythms, and to estimate the magnitude of rhythm-to-broadband modulation on a trial-by-trial basis. These tools revealed numerous coupling motifs between the phase of low-frequency (Ξ΄, ΞΈ, Ξ±, Ξ², and Ξ³ band) rhythms and the amplitude of broadband spectral change. In the ΞΈ and Ξ² ranges, the coupling of phase to broadband change is dynamic during visual processing, decreasing in some occipital areas and increasing in others, in a gyrally specific pattern. Finally, we demonstrate that the rhythms interact with one another across frequency ranges, and across cortical sites
Communicating Uncertainty and Risk in Air Quality Maps
Environmental sensors provide crucial data for understanding our
surroundings. For example, air quality maps based on sensor readings help users
make decisions to mitigate the effects of pollution on their health. Standard
maps show readings from individual sensors or colored contours indicating
estimated pollution levels. However, showing a single estimate may conceal
uncertainty and lead to underestimation of risk, while showing sensor data
yields varied interpretations. We present several visualizations of uncertainty
in air quality maps, including a frequency-framing "dotmap" and small
multiples, and we compare them with standard contour and sensor-based maps. In
a user study, we find that including uncertainty in maps has a significant
effect on how much users would choose to reduce physical activity, and that
people make more cautious decisions when using uncertainty-aware maps.
Additionally, we analyze think-aloud transcriptions from the experiment to
understand more about how the representation of uncertainty influences people's
decision-making. Our results suggest ways to design maps of sensor data that
can encourage certain types of reasoning, yield more consistent responses, and
convey risk better than standard maps
Statistical Analysis in Art Conservation Research
Evaluates all components of data analysis and shows that statistical methods in conservation are vastly underutilized. Also offers specific examples of possible improvements
Visualisation Techniques for Interpreting Machine Learning Models
With the increase of complex Machine Learning (ML) models making decisions in
everyday life in a wide range of fields from economics to healthcare, the demand
for Interpretable Machine Learning (IML) techniques has grown. One method to
broaden the understanding of the behaviour of a fitted ML model is through the
use of informative visualisations. Visualisations can aid in interpretation and can
provide a more thorough examination into the nature of the predictions generated
from an ML model. This is of particular importance when using so-called blackbox
models, such as random forests or Bayesian Additive Regression Trees (BART)
models.
In this thesis, various IML approaches are proposed through the use of novel
visualisations for displaying different metrics and model summaries which can be
used for examining the behaviour of a fitted ML model. First, we present flexible
methods for investigating variable importance, interactions, and variable effects by
presenting a suite of visualisations that can aid in the interpretation of statistical
and ML models through the use of model-specific and agnostic methods. Following
from this, motivated in part by the lack of existing visualisation methods and by
the rise in popularity of this particular model, we develop novel visualisations for
examining BART models that include examining the tree structures and, through
the posterior distribution, the uncertainty surrounding predictions. Lastly, we
demonstrate and discuss our implementation of the R package software vivid
(Variable Importance and Variable Interaction Displays) which is used to explore
the behaviour of fitted ML models. Here, we focus on key package features and
general architectural principles used in vivid when designing informative IML
visualisations and provide a practical illustration of the package in use
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Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :곡과λν μ»΄ν¨ν°κ³΅νλΆ,2020. 2. μμ§μ±.Understanding data through interactive visualization, also known as visual analytics, is a common and necessary practice in modern data science. However, as data sizes have increased at unprecedented rates, the computation latency of visualization systems becomes a significant hurdle to visual analytics. The goal of this dissertation is to design a series of systems for progressive visual analytics (PVA)βa visual analytics paradigm that can provide intermediate results during computation and allow visual exploration of these resultsβto address the scalability hurdle. To support the interactive exploration of data with billions of records, we first introduce SwiftTuna, an interactive visualization system with scalable visualization and computation components. Our performance benchmark demonstrates that it can handle data with four billion records, giving responsive feedback every few seconds without precomputation. Second, we present PANENE, a progressive algorithm for the Approximate k-Nearest Neighbor (AKNN) problem. PANENE brings useful machine learning methods into visual analytics, which has been challenging due to their long initial latency resulting from AKNN computation. In particular, we accelerate t-Distributed Stochastic Neighbor Embedding (t-SNE), a popular non-linear dimensionality reduction technique, which enables the responsive visualization of data with a few hundred columns. Each of these two contributions aims to address the scalability issues stemming from a large number of rows or columns in data, respectively. Third, from the users' perspective, we focus on improving the trustworthiness of intermediate knowledge gained from uncertain results in PVA. We propose a novel PVA concept, Progressive Visual Analytics with Safeguards, and introduce PVA-Guards, safeguards people can leave on uncertain intermediate knowledge that needs to be verified. We also present a proof-of-concept system, ProReveal, designed and developed to integrate seven safeguards into progressive data exploration. Our user study demonstrates that people not only successfully created PVA-Guards on ProReveal but also voluntarily used PVA-Guards to manage the uncertainty of their knowledge. Finally, summarizing the three studies, we discuss design challenges for progressive systems as well as future research agendas for PVA.νλ λ°μ΄ν° μ¬μ΄μΈμ€μμ μΈν°λν°λΈν μκ°νλ₯Ό ν΅ν΄ λ°μ΄ν°λ₯Ό μ΄ν΄νλ κ²μ νμμ μΈ λΆμ λ°©λ² μ€ νλμ΄λ€. κ·Έλ¬λ, μ΅κ·Ό λ°μ΄ν°μ ν¬κΈ°κ° νλ°μ μΌλ‘ μ¦κ°νλ©΄μ λ°μ΄ν° ν¬κΈ°λ‘ μΈν΄ λ°μνλ μ§μ° μκ°μ΄ μΈν°λν°λΈν μκ°μ λΆμμ ν° κ±Έλ¦Όλμ΄ λμλ€. λ³Έ μ°κ΅¬μμλ μ΄λ¬ν νμ₯μ± λ¬Έμ λ₯Ό ν΄κ²°νκΈ° μν΄ μ μ§μ μκ°μ λΆμ(Progressive Visual Analytics)μ μ§μνλ μΌλ ¨μ μμ€ν
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μ ꡬνν λμ λμμΈμ λμ μ ν₯ν μ°κ΅¬ λ°©ν₯μ λͺ¨μνλ€.CHAPTER1. Introduction 2
1.1 Background and Motivation 2
1.2 Thesis Statement and Research Questions 5
1.3 Thesis Contributions 5
1.3.1 Responsive and Incremental Visual Exploration of Large-scale Multidimensional Data 6
1.3.2 ProgressiveComputation of Approximate k-Nearest Neighbors and Responsive t-SNE 7
1.3.3 Progressive Visual Analytics with Safeguards 8
1.4 Structure of Dissertation 9
CHAPTER2. Related Work 11
2.1 Progressive Visual Analytics 11
2.1.1 Definitions 11
2.1.2 System Latency and Human Factors 13
2.1.3 Users, Tasks, and Models 15
2.1.4 Techniques, Algorithms, and Systems. 17
2.1.5 Uncertainty Visualization 19
2.2 Approaches for Scalable Visualization Systems 20
2.3 The k-Nearest Neighbor (KNN) Problem 22
2.4 t-Distributed Stochastic Neighbor Embedding 26
CHAPTER3. SwiTuna: Responsive and Incremental Visual Exploration of Large-scale Multidimensional Data 28
3.1 The SwiTuna Design 31
3.1.1 Design Considerations 32
3.1.2 System Overview 33
3.1.3 Scalable Visualization Components 36
3.1.4 Visualization Cards 40
3.1.5 User Interface and Interaction 42
3.2 Responsive Querying 44
3.2.1 Querying Pipeline 44
3.2.2 Prompt Responses 47
3.2.3 Incremental Processing 47
3.3 Evaluation: Performance Benchmark 49
3.3.1 Study Design 49
3.3.2 Results and Discussion 52
3.4 Implementation 56
3.5 Summary 56
CHAPTER4. PANENE:AProgressive Algorithm for IndexingandQuerying Approximate k-Nearest Neighbors 58
4.1 Approximate k-Nearest Neighbor 61
4.1.1 A Sequential Algorithm 62
4.1.2 An Online Algorithm 63
4.1.3 A Progressive Algorithm 66
4.1.4 Filtered AKNN Search 71
4.2 k-Nearest Neighbor Lookup Table 72
4.3 Benchmark. 78
4.3.1 Online and Progressive k-d Trees 78
4.3.2 k-Nearest Neighbor Lookup Tables 83
4.4 Applications 85
4.4.1 Progressive Regression and Density Estimation 85
4.4.2 Responsive t-SNE 87
4.5 Implementation 92
4.6 Discussion 92
4.7 Summary 93
CHAPTER5. ProReveal: Progressive Visual Analytics with Safeguards 95
5.1 Progressive Visual Analytics with Safeguards 98
5.1.1 Definition 98
5.1.2 Examples 101
5.1.3 Design Considerations 103
5.2 ProReveal 105
5.3 Evaluation 121
5.4 Discussion 127
5.5 Summary 130
CHAPTER6. Discussion 132
6.1 Lessons Learned 132
6.2 Limitations 135
CHAPTER7. Conclusion 137
7.1 Thesis Contributions Revisited 137
7.2 Future Research Agenda 139
7.3 Final Remarks 141
Abstract (Korean) 155
Acknowledgments (Korean) 157Docto
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