MARVIS: Modality Adaptive Reasoning over VISualizations

Benjamin Feuer; Chinmay Hegde; Lennart Purucker; Oussama Elachqar

arxiv: 2507.01544 · v2 · submitted 2025-07-02 · 💻 cs.LG

MARVIS: Modality Adaptive Reasoning over VISualizations

Benjamin Feuer , Lennart Purucker , Oussama Elachqar , Chinmay Hegde This is my paper

Pith reviewed 2026-05-19 05:51 UTC · model grok-4.3

classification 💻 cs.LG

keywords latent embeddingsvisualizationsvision-language modelsmodality adaptationcross-domain predictionmulti-modal reasoningembedding visualization

0 comments

The pith

Rendering latent embeddings as static visualizations enables one 3B VLM to deliver competitive predictions across vision, audio, biological, and tabular domains without any domain-specific training.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes converting internal representations of data from different modalities into images so that a general vision-language model can apply its existing spatial and fine-grained reasoning to solve prediction tasks. This single model replaces the usual collection of specialized predictors while still reaching strong results on both common and long-tail domains. A reader would care because the method removes the need to retrain or maintain separate systems for each new data type. The reported results show the approach narrowing the gap to expert models and even surpassing a larger general-purpose model on average.

Core claim

MARVIS converts latent embedding spaces from any input modality into visual representations and then relies on the spatial reasoning capabilities of a vision-language model to interpret those visualizations and produce accurate predictions. The same 3B-parameter model is used for all tasks and requires no per-domain fine-tuning or access to raw modality data.

What carries the argument

The conversion of latent embeddings into static visualizations that preserve task-relevant structure for VLM spatial reasoning.

If this is right

One model can handle predictive tasks in vision, audio, biological, and tabular domains at competitive levels.
Development of new predictors for additional modalities becomes unnecessary since no domain-specific training is required.
The performance difference between general VLMs and specialized domain models shrinks substantially.
Long-tail or non-traditional data types become more practical to work with using existing general-purpose reasoning skills.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same visualization step could be tested on additional modalities such as time-series or graph data by designing suitable rendering functions that expose structure visually.
Allowing the VLM to request or iteratively refine the visualizations during reasoning might increase accuracy on tasks where a single static image loses critical details.
If embedding spaces from unrelated domains share visually recognizable patterns, this could simplify the design of future multi-modal systems that avoid modality-specific encoders.

Load-bearing premise

Static images of latent embeddings retain enough task-relevant information for a general VLM to make accurate predictions without seeing the original raw data or receiving domain-specific training.

What would settle it

A clear drop in accuracy on a new modality or task when the same embeddings are rendered visually but the VLM fails to recover the necessary patterns from the resulting images would show the visualization step does not preserve sufficient structure.

Figures

Figures reproduced from arXiv: 2507.01544 by Benjamin Feuer, Chinmay Hegde, Lennart Purucker, Oussama Elachqar.

**Figure 1.** Figure 1: MARVIS transforms VLMs into strong predictors. MARVIS-3B achieves competitive performance with specialized baselines across modalities and domains, for regression, binary and MC classification, using ICL alone. MARVIS demonstrates the effectiveness of visual reasoning for diverse predictive tasks. 2 Problem Setting & Motivation In this section, we lay out in detail the challenges of using both FMs and spec… view at source ↗

**Figure 2.** Figure 2: The four-stage MARVIS pipeline starts with raw input data, captures key patterns using specialist embedding generating models, determines an appropriate strategy for plotting the data, and prompts a VLM with visual context, as well as (optionally) metadata and semantic context, then extracts predictions. Core Insight: Vision is a Skeleton Key. For predictive tasks, it is not usually the raw data that we wa… view at source ↗

**Figure 3.** Figure 3: Mean Accuracy by Configuration. Comparison of different visualization strategies showing that perturbation-based approaches with uncertainty analysis achieve the highest performance, followed by semantic axes with meaningful class labels. • Reduced heuristic reliance: -21% less usage of ”closest” heuristics, -20% less cluster-based reasoning These patterns suggest that VLMs engage in more thorough spatial … view at source ↗

**Figure 4.** Figure 4: Configuration Performance Heatmap. Detailed breakdown showing performance variations across different parameter combinations and visualization strategies. Darker regions indicate higher accuracy, with perturbation-based methods consistently showing superior performance across various settings. Baseline Robustness: Even the basic tsne approach achieves reasonable performance (45%), validating the fundamenta… view at source ↗

**Figure 5.** Figure 5: Critical Difference Plot for Classification Performance. Statistical analysis using balanced accuracy across OpenML CC18 datasets. Connected algorithms have no statistically significant difference (p ≥ 0.05) using the Nemenyi post-hoc test. MARVIS ranks competitively among traditional ML methods and significantly outperforms other LLM approaches [PITH_FULL_IMAGE:figures/full_fig_p021_5.png] view at source ↗

**Figure 6.** Figure 6: Classification Performance Matrix Heatmap. Dataset-wise performance comparison showing MARVIS consistency across different types of tabular classification tasks. Each row represents a dataset, and each column represents an algorithm. Darker colors indicate higher balanced accuracy scores. D.3 Regression Performance Analysis For regression tasks, MARVIS was evaluated on a custom benchmark of 43 regression d… view at source ↗

**Figure 7.** Figure 7: Critical Difference Plot for Regression Performance. Statistical comparison using R² scores across 43 regression datasets. MARVIS demonstrates statistically competitive performance with traditional methods, ranking in the middle tier without significant differences from top performers. Key correlation insights: • High Classification Alignment: 0.978 Pearson correlation indicates both methods excel on simil… view at source ↗

**Figure 8.** Figure 8: Regression Performance Matrix Heatmap. Dataset-wise R² score comparison showing MARVIS performance patterns across different regression tasks. The visualization reveals strengths in certain problem types while highlighting areas for potential improvement. Task Type Pearson r Spearmanρ Kendall τ Datasets Classification 0.978 0.945 0.823 65 Regression 0.884 0.867 0.698 41 [PITH_FULL_IMAGE:figures/full_fig_… view at source ↗

**Figure 9.** Figure 9: MARVIS vs TabPFN v2 Classification Correlation. Scatter plot showing strong positive correlation (r = 0.978) between MARVIS and TabPFN v2 balanced accuracy scores across OpenML CC18 datasets. Points above the diagonal line indicate datasets where MARVIS outperforms TabPFN v2. • CC18 Classification Tasks: 72 datasets from the OpenML CC18 benchmark suite • Regression Tasks: 41 carefully selected regression d… view at source ↗

**Figure 10.** Figure 10: MARVIS vs TabPFN v2 Regression Correlation. Scatter plot showing moderate positive correlation (r = 0.884) between MARVIS and TabPFN v2 R² scores across regression datasets. The correlation suggests similar strengths but with more divergent performance patterns compared to classification tasks. Feature: ”bkblk” (Chess Kr-vs-Kp dataset) Basic metadata: Binary feature (t/f) Semantic enhancement: ”Whether th… view at source ↗

read the original abstract

Predictive applications of machine learning often rely on small (sub 1 Bn parameter) specialized models tuned to particular domains or modalities. Such models often achieve excellent performance, but lack flexibility. LLMs and VLMs offer versatility, but typically underperform specialized predictors, especially on non-traditional modalities and long-tail domains. We propose MARVIS (Modality Adaptive Reasoning over VISualizations), a system that transforms latent embedding spaces into visual representations and then leverages the spatial and fine-grained reasoning skills of VLMs to interpret the visualizations and utilize them for predictions successfully. MARVIS achieves competitive performance across vision, audio, biological, and tabular domains using a single 3B parameter model, yielding results that beat Gemini 2.0 by 16% on average. MARVIS drastically reduces the gap between LLM/VLMs approaches and specialized domain-specific methods, without requiring any domain-specific training. Code and datasets are available at https://github.com/penfever/marvis.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

MARVIS renders embeddings as images for a small VLM to reason over and claims it beats Gemini 2.0 by 16% on average across modalities without fine-tuning, but the mechanism still needs checks.

read the letter

The main thing to know about this paper is that MARVIS renders embeddings from various modalities as images and lets a VLM reason over them to make predictions. It reports that a 3B parameter model can compete across vision, audio, biology, and tabular data, outperforming Gemini 2.0 by 16% on average with no domain-specific training. What is new is the consistent pipeline of turning non-visual embeddings into visual inputs for VLM use. This lets the model apply its spatial reasoning skills to data types it would not normally handle directly. The paper does a good job of demonstrating versatility in one model and making the code available for others to try. The results suggest a way to close the gap between general models and specialized ones. If the approach works as described, it could simplify building systems that handle mixed data sources. Where it is softer is in the validation of the visualization step. The concern is that static plots might lose critical structure, especially for sequential modalities. There are no ablations shown that isolate the effect of the rendering or compare to other ways of using the embeddings. This leaves open the possibility that the gains come from how the images are created or from prompt details rather than the reasoning itself. The abstract gives clear numbers but lacks the full experimental setup, so the soundness is moderate until the methods are checked. This is for researchers in multi-modal machine learning who want to explore general models for diverse data without training from scratch. It would be useful for someone thinking about embedding visualization techniques or efficient adaptation strategies. The paper engages honestly with the literature on VLMs and specialized models. I would recommend sending it for peer review to get feedback on the experiments and to confirm the claims.

Referee Report

3 major / 2 minor

Summary. The paper introduces MARVIS, which converts latent embeddings from vision, audio, biological, and tabular modalities into static visual representations that a general VLM then interprets for downstream predictions. Using a single 3B-parameter model without domain-specific fine-tuning, it reports competitive performance across these domains and an average 16% improvement over Gemini 2.0.

Significance. If the central mechanism holds, the work would be significant for showing that general VLMs can handle non-vision modalities via visualization, substantially narrowing the performance gap with specialized predictors while preserving model versatility and eliminating per-domain training.

major comments (3)

[Methods / Visualization pipeline] The visualization rendering step (described in the methods) is load-bearing for the claim that a general VLM extracts task-relevant structure from static images of embeddings. For sequential modalities such as audio, the reduction to a 2D image can collapse temporal dependencies; the manuscript provides no quantitative evidence (e.g., an ablation replacing the image with direct embedding input) that the VLM recovers this information rather than relying on prompt cues or dataset artifacts.
[Experiments / Main results] Results section, performance tables: the reported 16% average gain over Gemini 2.0 is presented without explicit controls for prompt engineering, visualization design choices, or whether the baseline receives the same rendered images versus raw modality data. This makes it impossible to attribute the improvement to the claimed mechanism.
[Ablations / Visualization variants] No ablation or sensitivity analysis is shown for the choice of dimensionality-reduction or plotting technique used to generate the visualizations; if performance varies sharply with these choices, the “parameter-free” and “domain-agnostic” framing in the abstract is undermined.

minor comments (2)

[Abstract] The abstract states that code and datasets are available at the GitHub link; the manuscript should include a brief description of the exact VLM checkpoint and rendering hyperparameters to support reproducibility.
[Figures] Figure captions and axis labels in the visualization examples could be expanded to indicate the exact embedding dimensionality and reduction method used for each modality.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We are grateful to the referee for the detailed and insightful review of our work on MARVIS. The comments highlight important aspects of our methodology and experimental design that we will address in the revision. Below, we provide point-by-point responses to the major comments.

read point-by-point responses

Referee: [Methods / Visualization pipeline] The visualization rendering step (described in the methods) is load-bearing for the claim that a general VLM extracts task-relevant structure from static images of embeddings. For sequential modalities such as audio, the reduction to a 2D image can collapse temporal dependencies; the manuscript provides no quantitative evidence (e.g., an ablation replacing the image with direct embedding input) that the VLM recovers this information rather than relying on prompt cues or dataset artifacts.

Authors: We concur that the visualization pipeline is central to MARVIS and that additional evidence would bolster the interpretation of our results. Regarding temporal dependencies in modalities like audio, our embedding visualizations aim to capture structural patterns through techniques such as scatter plots of reduced embeddings, which can preserve some sequential information via color or ordering in the plot. To directly address the request for quantitative evidence, we will include in the revised manuscript an ablation experiment. This will involve comparing VLM performance on the rendered visualizations against inputs where embeddings are provided as text (e.g., via serialization) or through alternative interfaces if available. We believe this will clarify that the VLM benefits from the visual format rather than solely from prompt engineering or artifacts. revision: yes
Referee: [Experiments / Main results] Results section, performance tables: the reported 16% average gain over Gemini 2.0 is presented without explicit controls for prompt engineering, visualization design choices, or whether the baseline receives the same rendered images versus raw modality data. This makes it impossible to attribute the improvement to the claimed mechanism.

Authors: Thank you for this important point on experimental controls. In our original setup, Gemini 2.0 was evaluated using its native multimodal capabilities on the raw data from each modality, while MARVIS processes all inputs through the visualization step before VLM reasoning. To improve attribution, we will revise the results section to include additional baselines where Gemini 2.0 is also provided with the rendered visualization images as input. Furthermore, we will expand the description of our prompting strategy and visualization parameters in the methods and appendix to facilitate replication and to demonstrate that the gains are not solely due to prompt variations. revision: yes
Referee: [Ablations / Visualization variants] No ablation or sensitivity analysis is shown for the choice of dimensionality-reduction or plotting technique used to generate the visualizations; if performance varies sharply with these choices, the “parameter-free” and “domain-agnostic” framing in the abstract is undermined.

Authors: We appreciate the referee's concern regarding the sensitivity to visualization choices. Our method employs standard, off-the-shelf dimensionality reduction techniques without modality-specific tuning, supporting the domain-agnostic claim. However, to address potential variability, we will add a new ablation study in the revised paper. This study will evaluate performance under different dimensionality reduction algorithms (t-SNE, UMAP, PCA) and variations in plotting hyperparameters. We expect this to show that while some variation exists, the overall competitive performance holds, thereby reinforcing rather than undermining the framing in the abstract. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical system with external VLM component

full rationale

The paper presents MARVIS as an empirical pipeline that renders latent embeddings as visualizations and feeds them to an off-the-shelf VLM for cross-modal prediction. No mathematical derivation chain, self-definitional quantities, fitted parameters renamed as predictions, or load-bearing self-citations appear in the abstract or described mechanism. Performance numbers are reported as experimental outcomes rather than quantities forced by construction from the paper's own inputs. The central claim therefore remains self-contained against external benchmarks and does not reduce to tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach assumes that standard embedding extractors already capture task-relevant information and that 2-D visualizations are sufficient to surface that information for a VLM; no new physical constants or ad-hoc entities are introduced.

axioms (1)

domain assumption Pre-trained embedding models produce representations that contain sufficient predictive signal for downstream tasks when rendered visually.
Invoked when the paper states that latent spaces from any modality can be directly visualized and interpreted by a VLM.

pith-pipeline@v0.9.0 · 5703 in / 1169 out tokens · 24915 ms · 2026-05-19T05:51:51.511964+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AlexanderDuality.lean alexander_duality_circle_linking unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

MARVIS transforms latent embedding spaces into visual representations and then leverages the spatial and fine-grained reasoning skills of VLMs to interpret the visualizations
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Dimensionality Reduction: Apply t-SNE to create 2D visualizations optimized for VLM processing

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

53 extracted references · 53 canonical work pages

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One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - 🏷 Class 0: Blue RGB(30, 119, 181) - 🏷 Class 1: Orange RGB(255, 127, 12) - 🏷 Class 2: Green RGB(43, 160, 43) - 🧪 Test points: Light Gray RGB(211, 211, 211) Dataset Context: Tabular data embedded using appropriate features Based on the position of the red star ( 🎯 query p...

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[37]

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One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **SPECTRAL**: Reveals manifold structure using graph-based relat...

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One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **SPECTRAL**: Reveals manifold structure using graph-based relat...

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One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 3 classes and 0 highlighted tes...

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One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **UMAP**: Preserves both local and global structure with clearer...

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One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 3 classes and 0 highlighted tes...

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One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **ISOMAP**: Preserves geodesic distances along the data manifold...

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1": Good credit risk - likely to repay loan

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Four different views of the same 3D space: Isometric, Front (XZ), Side (YZ), and Top (XY)

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A pie chart showing the distribution of the 5 nearest neighbors by class

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[109]

One red ⭐ star point which is the 🎯 query point I want you to classify

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[110]

Good credit risk (creditworthy)

Four different views of the same 3D space: Isometric, Front (XZ), Side (YZ), and Top (XY) Class Legend: - Good credit risk (creditworthy): Blue RGB(30, 119, 181) - Bad credit risk (not creditworthy): Orange RGB(255, 127, 12) - 🧪 Test points: Light Gray RGB(211, 211, 211) Semantic Axis Interpretation: • X-axis (8.6% var): Mixed factors • Y-axis (3.6% var):...

work page
[113]

Good credit risk (creditworthy)

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - Good credit risk (creditworthy): Blue RGB(30, 119, 181) - Bad credit risk (not creditworthy): Orange RGB(255, 127, 12) - 🧪 Test points: Light Gray RGB(211, 211, 211) Semantic Axis Interpretation: • X-axis (8.5% var): Mixed factors • Y-axis (3.6% var): Mixed factors Data...

work page
[116]

1": Good credit risk - likely to repay loan

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - Good credit risk (creditworthy): Blue RGB(30, 119, 181) - Bad credit risk (not creditworthy): Orange RGB(255, 127, 12) - 🧪 Test points: Light Gray RGB(211, 211, 211) Semantic Axis Interpretation: • X-axis (8.5% var): Mixed factors • Y-axis (3.6% var): Mixed factors Data...

work page
[119]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **SPECTRAL**: Reveals manifold structure using graph-based relat...

work page
[122]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 2 classes and highlighted test ...

work page
[125]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **ISOMAP**: Preserves geodesic distances along the data manifold...

work page
[128]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **T-SNE**: t-SNE visualization of tabular data - **ISOMAP**: Preserves geodesic distances along the data manifold - **MDS**: Preserves pairwise distances between data points Dataset Cont...

work page
[131]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 2 classes and highlighted test ...

work page
[134]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **SPECTRAL**: Reveals manifold structure using graph-based relat...

work page
[137]

Good credit risk (creditworthy)

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 2 classes and highlighted test ...

work page
[140]

1": Good credit risk - likely to repay loan

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 2 classes and highlighted test ...

work page
[143]

Good credit risk (creditworthy)

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 2 classes and highlighted test ...

work page
[146]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **UMAP**: Preserves both local and global structure with clearer...

work page
[149]

Good credit risk (creditworthy)

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 2 classes and highlighted test ...

work page
[152]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **DECISION-SVC**: Decision-SVC visualization of tabular data Dataset Context: Tabular data with 2 classes and hig...

work page
[153]

Colored points representing training data, where each color corresponds to a different class

work page
[154]

Gray square points representing 🧪 test data

work page
[155]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **ISOMAP**: Preserves geodesic distances along the data manifold...

work page

[1] [3]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - 🏷 Class 0: Blue RGB(30, 119, 181) - 🏷 Class 1: Orange RGB(255, 127, 12) - 🏷 Class 2: Green RGB(43, 160, 43) - 🧪 Test points: Light Gray RGB(211, 211, 211) Dataset Context: Tabular data embedded using appropriate features Based on the position of the red star ( 🎯 query p...

work page

[2] [6]

No-use",

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - No-use: Blue RGB(30, 119, 181) - Long-term methods: Orange RGB(255, 127, 12) - Short-term methods: Green RGB(43, 160, 43) - 🧪 Test points: Light Gray RGB(211, 211, 211) Dataset Context: Tabular data embedded using appropriate features Based on the position of the red st...

work page

[3] [9]

No-use",

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - No-use: Blue RGB(30, 119, 181) - Long-term methods: Orange RGB(255, 127, 12) - Short-term methods: Green RGB(43, 160, 43) - 🧪 Test points: Light Gray RGB(211, 211, 211) Semantic Axis Interpretation: • X-axis (39.3% var): +Living standard (1=low, 2, 3, 4=high) • Y-axis (...

work page

[4] [12]

1": No-use

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - No-use: Blue RGB(30, 119, 181) - Long-term methods: Orange RGB(255, 127, 12) - Short-term methods: Green RGB(43, 160, 43) - 🧪 Test points: Light Gray RGB(211, 211, 211) Semantic Axis Interpretation: • X-axis (39.3% var): +Living standard (1=low, 2, 3, 4=high) • Y-axis (...

work page 1987

[5] [17]

Class_0",

The pie chart includes class counts, percentages, and average distances to neighbors Class Legend: - 🏷 Class 0: Blue RGB(30, 119, 181) - 🏷 Class 1: Orange RGB(255, 127, 12) - 🏷 Class 2: Green RGB(43, 160, 43) - 🧪 Test points: Light Gray RGB(211, 211, 211) K-NN Analysis (k=5): • 🏷 Class 0: 3 neighbors (60%), AvgDist: 8.0 • 🏷 Class 1: 1 neighbors (20%), Avg...

work page

[6] [20]

No-use",

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - No-use: Blue RGB(30, 119, 181) - Long-term methods: Orange RGB(255, 127, 12) - Short-term methods: Green RGB(43, 160, 43) - 🧪 Test points: Light Gray RGB(211, 211, 211) Semantic Axis Interpretation: • X-axis (39.3% var): +Living standard (1=low, 2, 3, 4=high) • Y-axis (...

work page

[7] [24]

Class_0",

Four different views of the same 3D space: Isometric, Front (XZ), Side (YZ), and Top (XY) Class Legend: - 🏷 Class 0: Blue RGB(30, 119, 181) - 🏷 Class 1: Orange RGB(255, 127, 12) - 🏷 Class 2: Green RGB(43, 160, 43) - 🧪 Test points: Light Gray RGB(211, 211, 211) This visualization shows 4 different viewing angles of the same 3D t-SNE space: - Isometric View...

work page

[8] [28]

No-use",

Four different views of the same 3D space: Isometric, Front (XZ), Side (YZ), and Top (XY) Class Legend: - No-use: Blue RGB(30, 119, 181) - Long-term methods: Orange RGB(255, 127, 12) - Short-term methods: Green RGB(43, 160, 43) - 🧪 Test points: Light Gray RGB(211, 211, 211) This visualization shows 4 different viewing angles of the same 3D t-SNE space: - ...

work page

[9] [31]

No-use",

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - No-use: Blue RGB(30, 119, 181) - Long-term methods: Orange RGB(255, 127, 12) - Short-term methods: Green RGB(43, 160, 43) - 🧪 Test points: Light Gray RGB(211, 211, 211) Semantic Axis Interpretation: • X-axis (39.3% var): +Living standard (1=low, 2, 3, 4=high) • Y-axis (...

work page

[10] [34]

1": No-use

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - No-use: Blue RGB(30, 119, 181) - Long-term methods: Orange RGB(255, 127, 12) - Short-term methods: Green RGB(43, 160, 43) - 🧪 Test points: Light Gray RGB(211, 211, 211) Semantic Axis Interpretation: • X-axis (39.3% var): +Living standard (1=low, 2, 3, 4=high) • Y-axis (...

work page 1987

[11] [37]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **SPECTRAL**: Reveals manifold structure using graph-based relat...

work page

[12] [40]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 3 classes and 0 highlighted tes...

work page

[13] [43]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **ISOMAP**: Preserves geodesic distances along the data manifold...

work page

[14] [46]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **T-SNE**: t-SNE visualization of tabular data - **ISOMAP**: Preserves geodesic distances along the data manifold - **MDS**: Preserves pairwise distances between data points Dataset Cont...

work page

[15] [49]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 3 classes and 0 highlighted tes...

work page

[16] [52]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **SPECTRAL**: Reveals manifold structure using graph-based relat...

work page

[17] [55]

No-use",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 3 classes and 0 highlighted tes...

work page

[18] [58]

1": No-use

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 3 classes and 0 highlighted tes...

work page 1987

[19] [61]

No-use",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 3 classes and 0 highlighted tes...

work page

[20] [64]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **UMAP**: Preserves both local and global structure with clearer...

work page

[21] [67]

No-use",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 3 classes and 0 highlighted tes...

work page

[22] [70]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **DECISION-SVC**: Decision-SVC visualization of tabular data Dataset Context: Tabular data with 3 classes and 0 h...

work page

[23] [73]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **FREQUENT-PATTERNS**: Frequent-Patterns visualization of tabular data Dataset Context: Tabular data with 3 class...

work page

[24] [76]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **ISOMAP**: Preserves geodesic distances along the data manifold...

work page

[25] [79]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - 🏷 Class 0: Blue RGB(30, 119, 181) - 🏷 Class 1: Orange RGB(255, 127, 12) - 🧪 Test points: Light Gray RGB(211, 211, 211) Dataset Context: Tabular data embedded using appropriate features Based on the position of the red star ( 🎯 query point) relative to the colored traini...

work page

[26] [82]

Good credit risk (creditworthy)

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - Good credit risk (creditworthy): Blue RGB(30, 119, 181) - Bad credit risk (not creditworthy): Orange RGB(255, 127, 12) - 🧪 Test points: Light Gray RGB(211, 211, 211) Dataset Context: Tabular data embedded using appropriate features Based on the position of the red star ...

work page

[27] [85]

Good credit risk (creditworthy)

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - Good credit risk (creditworthy): Blue RGB(30, 119, 181) - Bad credit risk (not creditworthy): Orange RGB(255, 127, 12) - 🧪 Test points: Light Gray RGB(211, 211, 211) Semantic Axis Interpretation: • X-axis (8.5% var): Mixed factors • Y-axis (3.6% var): Mixed factors Data...

work page

[28] [88]

1": Good credit risk - likely to repay loan

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - Good credit risk (creditworthy): Blue RGB(30, 119, 181) - Bad credit risk (not creditworthy): Orange RGB(255, 127, 12) - 🧪 Test points: Light Gray RGB(211, 211, 211) Semantic Axis Interpretation: • X-axis (8.5% var): Mixed factors • Y-axis (3.6% var): Mixed factors Data...

work page

[29] [93]

Class_0",

The pie chart includes class counts, percentages, and average distances to neighbors Class Legend: - 🏷 Class 0: Blue RGB(30, 119, 181) - 🏷 Class 1: Orange RGB(255, 127, 12) - 🧪 Test points: Light Gray RGB(211, 211, 211) Dataset Context: Tabular data embedded using appropriate features IMPORTANT: The pie chart shows the class distribution of the 5 nearest ...

work page

[30] [97]

Four different views of the same 3D space: Isometric, Front (XZ), Side (YZ), and Top (XY)

work page

[31] [98]

A pie chart showing the distribution of the 5 nearest neighbors by class

work page

[32] [99]

Class_0",

The pie chart includes class counts, percentages, and average distances to neighbors Class Legend: - 🏷 Class 0: Blue RGB(30, 119, 181) - 🏷 Class 1: Orange RGB(255, 127, 12) - 🧪 Test points: Light Gray RGB(211, 211, 211) Dataset Context: Tabular data embedded using appropriate features IMPORTANT: The pie chart shows the class distribution of the 5 nearest ...

work page

[33] [102]

Good credit risk (creditworthy)

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - Good credit risk (creditworthy): Blue RGB(30, 119, 181) - Bad credit risk (not creditworthy): Orange RGB(255, 127, 12) - 🧪 Test points: Light Gray RGB(211, 211, 211) Semantic Axis Interpretation: • X-axis (8.5% var): Mixed factors • Y-axis (3.6% var): Mixed factors Data...

work page

[34] [106]

Class_0",

Four different views of the same 3D space: Isometric, Front (XZ), Side (YZ), and Top (XY) Class Legend: - 🏷 Class 0: Blue RGB(30, 119, 181) - 🏷 Class 1: Orange RGB(255, 127, 12) - 🧪 Test points: Light Gray RGB(211, 211, 211) Dataset Context: Tabular data embedded using appropriate features Based on the position of the red star ( 🎯 query point) relative to...

work page

[35] [109]

One red ⭐ star point which is the 🎯 query point I want you to classify

work page

[36] [110]

Good credit risk (creditworthy)

Four different views of the same 3D space: Isometric, Front (XZ), Side (YZ), and Top (XY) Class Legend: - Good credit risk (creditworthy): Blue RGB(30, 119, 181) - Bad credit risk (not creditworthy): Orange RGB(255, 127, 12) - 🧪 Test points: Light Gray RGB(211, 211, 211) Semantic Axis Interpretation: • X-axis (8.6% var): Mixed factors • Y-axis (3.6% var):...

work page

[37] [113]

Good credit risk (creditworthy)

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - Good credit risk (creditworthy): Blue RGB(30, 119, 181) - Bad credit risk (not creditworthy): Orange RGB(255, 127, 12) - 🧪 Test points: Light Gray RGB(211, 211, 211) Semantic Axis Interpretation: • X-axis (8.5% var): Mixed factors • Y-axis (3.6% var): Mixed factors Data...

work page

[38] [116]

1": Good credit risk - likely to repay loan

One red ⭐ star point which is the 🎯 query point I want you to classify Class Legend: - Good credit risk (creditworthy): Blue RGB(30, 119, 181) - Bad credit risk (not creditworthy): Orange RGB(255, 127, 12) - 🧪 Test points: Light Gray RGB(211, 211, 211) Semantic Axis Interpretation: • X-axis (8.5% var): Mixed factors • Y-axis (3.6% var): Mixed factors Data...

work page

[39] [119]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **SPECTRAL**: Reveals manifold structure using graph-based relat...

work page

[40] [122]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 2 classes and highlighted test ...

work page

[41] [125]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **ISOMAP**: Preserves geodesic distances along the data manifold...

work page

[42] [128]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **T-SNE**: t-SNE visualization of tabular data - **ISOMAP**: Preserves geodesic distances along the data manifold - **MDS**: Preserves pairwise distances between data points Dataset Cont...

work page

[43] [131]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 2 classes and highlighted test ...

work page

[44] [134]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **SPECTRAL**: Reveals manifold structure using graph-based relat...

work page

[45] [137]

Good credit risk (creditworthy)

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 2 classes and highlighted test ...

work page

[46] [140]

1": Good credit risk - likely to repay loan

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 2 classes and highlighted test ...

work page

[47] [143]

Good credit risk (creditworthy)

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 2 classes and highlighted test ...

work page

[48] [146]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **UMAP**: Preserves both local and global structure with clearer...

work page

[49] [149]

Good credit risk (creditworthy)

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data Dataset Context: Tabular data with 2 classes and highlighted test ...

work page

[50] [152]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **DECISION-SVC**: Decision-SVC visualization of tabular data Dataset Context: Tabular data with 2 classes and hig...

work page

[51] [153]

Colored points representing training data, where each color corresponds to a different class

work page

[52] [154]

Gray square points representing 🧪 test data

work page

[53] [155]

Class_0",

One red ⭐ star point which is the 🎯 query point I want you to classify The multiple visualizations provide different perspectives on the same underlying data structure: - **PCA**: Shows linear relationships and directions of maximum variance - **T-SNE**: t-SNE visualization of tabular data - **ISOMAP**: Preserves geodesic distances along the data manifold...

work page