Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Train Test Split | Recognizing Handwritten Digits
Recognizing Handwritten Digits
course content

Contenido del Curso

Recognizing Handwritten Digits

Train Test Split

In Python, the train_test_split function, part of the sklearn.model_selection module, is frequently utilized for dividing a dataset into two parts: a training subset and a testing subset.

This train_test_split() function performs a random partitioning of the dataset into these subsets, determined by a predefined test size or train size.

Tarea

  1. Split the dataset into training and test sets. Use only the first 1000 samples for splitting.
  2. Print the shapes and sizes of the resulting training and test sets for both the feature matrix and the target vector.

Tarea

  1. Split the dataset into training and test sets. Use only the first 1000 samples for splitting.
  2. Print the shapes and sizes of the resulting training and test sets for both the feature matrix and the target vector.

Mark tasks as Completed
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

In Python, the train_test_split function, part of the sklearn.model_selection module, is frequently utilized for dividing a dataset into two parts: a training subset and a testing subset.

This train_test_split() function performs a random partitioning of the dataset into these subsets, determined by a predefined test size or train size.

Tarea

  1. Split the dataset into training and test sets. Use only the first 1000 samples for splitting.
  2. Print the shapes and sizes of the resulting training and test sets for both the feature matrix and the target vector.

Mark tasks as Completed
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 1. Capítulo 6
AVAILABLE TO ULTIMATE ONLY
We're sorry to hear that something went wrong. What happened?
some-alt