{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Amazon SageMaker Autopilot Candidate Definition Notebook\n", "\n", "This notebook was automatically generated by the AutoML job **automl-dm-1675608463**.\n", "This notebook allows you to customize the candidate definitions and execute the SageMaker Autopilot workflow.\n", "\n", "The dataset has **2** columns and the column named **sentiment** is used as\n", "the target column. This is being treated as a **MulticlassClassification** problem. The dataset also has **3** classes.\n", "This notebook will build a **[MulticlassClassification](https://en.wikipedia.org/wiki/Multiclass_classification)** model that\n", "**maximizes** the \"**ACCURACY**\" quality metric of the trained models.\n", "The \"**ACCURACY**\" metric provides the percentage of times the model predicted the correct class.\n", "\n", "As part of the AutoML job, the input dataset has been randomly split into two pieces, one for **training** and one for\n", "**validation**. This notebook helps you inspect and modify the data transformation approaches proposed by Amazon SageMaker Autopilot. You can interactively\n", "train the data transformation models and use them to transform the data. Finally, you can execute a multiple algorithm hyperparameter optimization (multi-algo HPO)\n", "job that helps you find the best model for your dataset by jointly optimizing the data transformations and machine learning algorithms.\n", "\n", "
endpoint_name
in the previous code block.\n",
"