There are 4 float features in the classic Iris dataset, and 150 rows of data, so there are 150×4=600150\times 4=600150×4=600 float values in total for the inputs.

Quick Scoop: What “floats in iris” usually means

When people ask “how many floats in iris,” they’re almost always talking about the Fisher / scikit‑learn Iris dataset used in machine learning demos.

In that dataset:

  • There are 150 iris flower samples (rows).
  • Each sample has 4 numeric features, all stored as floats:
    • Sepal length
    • Sepal width
    • Petal length
    • Petal width
  • So:
    • Number of float features per sample: 4
    • Total number of float values: 150×4=600150\times 4=600150×4=600.

The label (species) is typically stored as an integer (0, 1, 2) rather than a float.

Mini example

If you load the dataset from a common ML library, you usually get:

  • A feature matrix of shape 150×4 (all floats).
  • A target vector of length 150 (integers for Setosa, Versicolor, Virginica).

So in everyday coding terms: X has 600 float numbers, y has 150 integers.

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