Data Dimensionality Reduction

Input Data (JSON Array)

Dimensionality Reduction Configuration

Example Input

[
  {
    "id": 1,
    "sepal_length": 5.1,
    "sepal_width": 3.5,
    "petal_length": 1.4,
    "petal_width": 0.2,
    "species": "setosa"
  },
  {
    "id": 2,
    "sepal_length": 4.9,
    "sepal_width": 3.0,
    "petal_length": 1.4,
    "petal_width": 0.2,
    "species": "setosa"
  },
  {
    "id": 3,
    "sepal_length": 7.0,
    "sepal_width": 3.2,
    "petal_length": 4.7,
    "petal_width": 1.4,
    "species": "versicolor"
  }
]

Reduction Methods:

  • PCA: Linear dimensionality reduction that preserves maximum variance
  • t-SNE: Non-linear dimensionality reduction that preserves local structure
  • Higher explained variance ratios (PCA) or lower KL divergence (t-SNE) indicate better reduction quality