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You’re interested in machine learning data visualization. How can you make the most of your tools?

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Machine learning data visualization is a powerful way to explore, understand, and communicate your data and models. But how can you make the most of your tools and avoid common pitfalls? Here are some tips and best practices to help you create effective and engaging visualizations.

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1 Choose the right tool

When it comes to machine learning data visualization, there are many tools and libraries to choose from, such as matplotlib, seaborn, plotly, bokeh, and more. Each one has its own unique strengths, weaknesses, and learning curve. To decide which one is best for you, consider the type and size of your data, the complexity and interactivity of your visualization, the format and purpose of your output, as well as its compatibility and integration with your workflow and environment.

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Yodi Aditya

Exploring Agentic, Small LLM and RL
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Usually, I use Purpose, Complexity, and Format as three-pointers to help me choose the right visualization tools. (1) Matplotlib and Seaborn: Its excellent for internal analysis and model-building purposes. Time series, categorical and large data is work well for this static visualization. (2) Bokeh and plotly: Great for non-technical stakeholders that may require interactive visualization. It's useful to build dashboard and report to end-user where its offer many functionalities. (3) Always start with simple visualization and gradually pick the more advanced version. Use user feedbacks to understand what missing from current visualization and how to choose to communicate better.

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2 Know your audience

Before you start creating your visualization, it's important to consider who your audience is and what they want to learn from your data. This could be yourself, your team, your clients, or the general public. You should adjust your design, style, and language accordingly to make your visualization accessible, informative, and appealing. Ask yourself questions like: what is the level of expertise and background knowledge of your audience? What is the main message or insight you want to convey? What is the best way to present your data and model? How can you capture and maintain the attention of your audience? Answering these questions will help ensure that you create a successful visualization.

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Yodi Aditya

Exploring Agentic, Small LLM and RL
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Simple, Detailed, Interactive, and Advanced Real-time are four category visualization types that I use to deal with multiple stakeholders in business. (1) Simple: For internal teams that already understand the operation metrics. Bar, pie-chart often used. (2) Detailed: For cross-technical team meetings where stakeholders will have very detailed questions. Heatmap and scatter plot often used. (3) Interactive: Shareholders meeting with boards, CFO, CEO or another individual who can go from top to detail while only expert in one or few areas. Dashboard, web-based interactive with summary often used (4) Real-time: To specific teams that need to monitor progress every minute. Line, bar but update in real-time.

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3 Use appropriate techniques

Machine learning data visualization involves a variety of techniques, such as scatter plots, histograms, box plots, heat maps, bar charts, line charts, and more. You should select the technique that best represents your data and model, taking into account the dimensionality and distribution of your data, the type and scale of your variables, the relationship and correlation between your variables, as well as the patterns and trends in your data and model. Each technique has its own advantages and disadvantages to consider.

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Yodi Aditya

Exploring Agentic, Small LLM and RL
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Boxplot is surprisingly powerful in presenting complex information. For example: (1) Compare Model performance and single evaluation metrics. Put XGBoost, FNN, RNN-LSTM into X-axis and evaluation metrics (eg: RMSE) as Y-axis. You can see how each model performs on the same loss metric easily. (2) Or, we can combine all model and performance metrics altogether. Eg: Y-axis will be 0.5 to 1 and each one model will have four boxplot (F1, Accuracy, Precision and Recall) (3) The beauty of Boxplot rely on its ability to show the distribution and outliers, which very useful to avoid over-generalization on complex problem.

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4 Follow design principles

Machine learning data visualization is both a technical and creative task, so it's important to follow certain design principles to make your visualization effective and attractive. Simplicity is key - focus on the essential elements and avoid clutter and noise. Additionally, you should aim for consistency across your visualization by using a coherent color scheme, font, layout, and style. Contrast is also important - use different colors, shapes, sizes, and positions to highlight relevant features of your data and model. Balance is also essential - distribute the visual weight and space evenly without overcrowding or leaving empty areas. Furthermore, make sure to align the elements of your visualization along a common edge, axis, or center. Finally, organize the elements according to their importance, order, or category to create hierarchy.

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5 Add context and explanation

Machine learning data visualization should be supplemented with context and explanation. Labels, titles, legends, captions, annotations, and other elements should be added to provide understanding of the data and model. These elements should be clear, concise, and accurate to help your audience comprehend the visualization. To achieve this, use descriptive and meaningful labels and titles for axes, variables, and charts. Legends and color bars can explain the meaning and scale of colors, shapes, and sizes. Captions and annotations can offer additional information or commentary on the data and model. If relevant and helpful, use

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You’re interested in machine learning data visualization. How can you make the most of your tools?

1

2

3

4

5

1 Choose the right tool

2 Know your audience

3 Use appropriate techniques

4 Follow design principles

5 Add context and explanation

Algorithms

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You’re interested in machine learning data visualization. How can you make the most of your tools?

1

2

3

4

5

1 Choose the right tool

2 Know your audience

3 Use appropriate techniques

4 Follow design principles

5 Add context and explanation

Algorithms

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