Data Encoding (DP_3)
This functionality converts data to a recognizable format through encoding. Supported techniques including, but are not limited to, one-hot, label and categorical encoding.
This process involves converting data into a format that can be understood by a computer. This can include converting text into numerical values, or categorizing data into discrete groups. The goal of encoding is to make it possible for a machine learning algorithm to interpret and learn from the data.
There are many different types of encoding techniques, such as one-hot encoding, which converts categorical data into a binary format, and label encoding, which assigns a unique numerical value to each category in a categorical variable. The appropriate encoding technique depends on the type of data and the machine learning algorithm being used.
Sample Request
This request encodes categorical values in the X variable with one-hot
method, ignoring values in the Y variable.
Encoding Data
Encode categorical values
POST
https://autogon.ai/api/v1/engine/start
Encodes categorical data on specific columns with specified boundaries
Request Body
Last updated