Visualization Results
Interpretation
Formula Used
Min-max scaling maps every value into a common interval. It preserves order while changing magnitude. This makes differently measured features easier to compare.
Z-score scaling centers values around the feature mean. It measures distance in standard deviations. Positive values exceed the mean for that feature.
Robust scaling uses the median and interquartile range. It reduces influence from extreme observations. This supports clearer comparisons across noisy machine learning data.
How to Use
- Paste data or upload a supported file.
- Select feature, identifier, target, and group columns.
- Choose scaling, transformation, and missing-value settings.
- Apply filters or sampling for large datasets.
- Reorder axes and customize plot appearance.
- Generate the plot and inspect statistics.
- Export the visualization, data, or saved settings.
Example Data
| ID | Sepal Length | Sepal Width | Petal Length | Petal Width | Class |
|---|---|---|---|---|---|
| I-001 | 5.1 | 3.5 | 1.4 | 0.2 | Setosa |
| I-002 | 6.4 | 3.2 | 4.5 | 1.5 | Versicolor |
| I-003 | 6.3 | 3.3 | 6.0 | 2.5 | Virginica |
Frequently Asked Questions
What is a parallel coordinates plot?
It displays multidimensional records as connected lines across parallel axes. Each axis represents one variable. Similar line paths can reveal groups and patterns.
Which columns should be plotted?
Choose numeric features that support your analysis objective. Avoid identifiers as numeric axes. Put labels or classes in grouping controls.
Why should features be scaled?
Features often use different units and numeric ranges. Scaling makes their relative positions comparable. It can expose structure hidden by magnitude differences.
How are categorical groups colored?
The calculator assigns each distinct group a numeric code. Plot colors follow those codes. The legend keeps original category names visible.
How does brushing work?
Drag inside an axis to select a value range. Multiple axes can hold constraints. Plotly highlights records satisfying every active condition.
How are missing values handled?
You can remove rows or replace missing values. Mean, median, zero, and custom methods are available. A separate level can preserve incomplete records.
What does outlier detection use?
The calculator uses the interquartile range rule per feature. Rows outside standard fences are flagged. This is a screening method, not final proof.
Can large datasets be plotted?
Yes, but too many lines cause visual clutter. Random or stratified sampling improves readability. Use maximum-line controls for browser performance.
Can I save the chart configuration?
Yes, export settings as a JSON file. Restore that file during another session. Data itself is excluded unless separately exported.