Great question! You've likely run the script and are now looking at the large array of numbers.
Here's how to interpret the output you're seeing from print(iris.data):
- Rows are Samples/Flowers: Each line that looks like
[5.1 3.5 1.4 0.2]represents a single Iris flower. There are 150 such rows in total, one for each flower in the dataset. - Columns are Features/Measurements: Within each row, the numbers correspond to different measurements of that particular flower. For the Iris dataset, these 4 measurements (features) are typically:
- Sepal Length (cm)
- Sepal Width (cm)
- Petal Length (cm)
- Petal Width (cm)
So, for example, the first row [5.1 3.5 1.4 0.2] means the first flower sample has a sepal length of 5.1 cm, a sepal width of 3.5 cm, a petal length of 1.4 cm, and a petal width of 0.2 cm.
This data array is the input that machine learning models will use to learn and make predictions.
