ML 1.4Lesson outlineLesson 4 of 5 in Module 1

Samples, Features and Labels

Turn a real table into `X` and `y`, and name each column's type.

Free sample. Everything in this module opens with a free account.Video in production.

Lesson outline: Samples, Features and Labels

This lesson stands on its own. These go deeper into what it uses.

The words this lesson introduces, each in one line. The module’s glossary collects them all, and the AI glossary has every term across the courses.

sample: One row of the table, one thing being described: a car.
label (target) (reminder, ML 1.1 and Maths 00.1): The column the model predicts: mpg.
feature: A column the model uses to predict the label.
feature matrix: The table of features, one row per sample, written X.
label vector: The labels, one per sample, written y.
shape: The number of rows, and of columns, of an array: (398, 7); a vector's has one number, (398,).
numerical: A column whose values are amounts; continuous when any value in a range is possible, discrete when it counts.
categorical: A column whose values name groups, even when they are stored as numbers.
time (narration): A column whose order matters and whose differences mean something, such as the model year.
text data: A column of free text.
ordinal: Categories with an order, such as a kitchen graded excellent to poor.
image data: Pixel values, one feature per pixel.
missing value: A cell with no recorded value, read by pandas as NaN.
feature space (reminder, Maths 2.1): The space with one dimension per feature, in which each sample is a point.