Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Declare Feature Vector and Target Variable | Clustering Demystified
Clustering Demystified
course content

Contenido del Curso

Clustering Demystified

bookDeclare Feature Vector and Target Variable

A feature vector is a set of numerical features that represent an object or sample. In machine learning, a feature vector is used as input to a model, and it typically contains multiple features that describe the characteristics of the object.

A target variable, also known as a response or dependent variable, is the variable that the model is trying to predict. It is the output of the model, and it is typically a numerical or categorical value.

For example, in a supervised learning problem where we want to predict the price of a house, the feature vector might include things like the number of bedrooms, square footage, and neighborhood, while the target variable would be the price of the house.

Methods description

The indexing [] operator is used to select specific columns from the DataFrame. For example, data[column_name] retrieves the column named "column_name" from the DataFrame data.

Tarea
test

Swipe to show code editor

  1. Declare feature vector (entire data).
  2. Declare the target variable ("status_type" column).

Mark tasks as Completed
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

A feature vector is a set of numerical features that represent an object or sample. In machine learning, a feature vector is used as input to a model, and it typically contains multiple features that describe the characteristics of the object.

A target variable, also known as a response or dependent variable, is the variable that the model is trying to predict. It is the output of the model, and it is typically a numerical or categorical value.

For example, in a supervised learning problem where we want to predict the price of a house, the feature vector might include things like the number of bedrooms, square footage, and neighborhood, while the target variable would be the price of the house.

Methods description

The indexing [] operator is used to select specific columns from the DataFrame. For example, data[column_name] retrieves the column named "column_name" from the DataFrame data.

Tarea
test

Swipe to show code editor

  1. Declare feature vector (entire data).
  2. Declare the target variable ("status_type" column).

Mark tasks as Completed
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 1. Capítulo 5
AVAILABLE TO ULTIMATE ONLY
We're sorry to hear that something went wrong. What happened?
some-alt