AutoML_Regression

This transform chooses the best algorithm to create models based on the train data. Subsequently, this created model is used on the test data to evaluate the model performance and make predictions. It predicts a continuous numeric output such as predict the car price or temperature.

tags: [“EDA”]

Parameters

The table gives a brief description about each parameter in AutoML_Regression transform.

Name:

By default, the transform name is populated. You can also add a custom name for the transform.

Train data:

The train dataset on which the algorithm is used to create models.

Test data:

The test dataset on which the created model is used to evaluate the performance and make predictions.

Run Mode:

The mode is by default set to Auto.

Target column:

The column on which predictions should be made.

Model Prediction:

The filename with which the output dataset is created and contains the model predictions.

Model Performance:

The filename with which the output dataset is created and contains the model performance.

Sample input for Auto ML Regression transform:

The output after running the Auto ML Regression transform on the dataset appears as below for model performance:

How to use it in Notebook

The following is the code snippet you must use in the Jupyter Notebook editor to run the **AutoML_Regression**transform: