Automated Machine Learning for Classification

This tutorial demonstrates how to use Azure Machine Learning's Automated Machine Learning (AutoML) feature to build and deploy a classification model without extensive coding.

Introduction to Automated ML

Automated ML simplifies the process of training machine learning models. It automatically explores various algorithms, feature engineering techniques, and hyperparameters to find the best model for your task. For classification, this means automatically selecting algorithms like Logistic Regression, LightGBM, or even neural networks, and tuning them to predict a categorical outcome.

Prerequisites

An Azure Subscription.
An Azure Machine Learning workspace.
The Azure CLI installed and configured, or the Azure Machine Learning SDK for Python.
A dataset suitable for classification.

Steps

Prepare Your Data

Ensure your dataset is clean and ready for training. For classification, you need a target column that contains the categorical labels you want to predict.

Example data structure:
```
CustomerID,Gender,Age,AnnualIncome,SpendingScore,Purchased
1,Male,19,15,39,0
2,Female,21,15,81,0
3,Female,20,20,6,0
...
```
In this example, Purchased is the target column.

Create an AutoML Classification Job

You can create an AutoML job using the Azure Machine Learning SDK or the Azure ML studio UI. The SDK provides more programmatic control.

Here's a conceptual Python snippet using the SDK:


from azure.ai.ml import MLClient
from azure.ai.ml.automl import automl_classifier
from azure.ai.ml.entities import Data
from azure.identity import DefaultAzureCredential

# Connect to your workspace
ml_client = MLClient(
    DefaultAzureCredential(),
    subscription_id="your_subscription_id",
    resource_group_name="your_resource_group",
    workspace_name="your_workspace_name"
)

# Load your training data (assuming it's registered in the workspace)
training_data = ml_client.data.get(name="your-classification-dataset", version="1")

# Configure the AutoML job
classification_job = automl_classifier(
    target_column_name="Purchased",
    primary_metric="accuracy",
    training_data=training_data,
    compute="your-compute-cluster", # Name of your compute cluster
    experiment_name="automl-classification-tutorial",
    n_cross_validations=5,
    enable_early_stopping=True,
    training_job_name="automl-classification-run"
)

# Submit the job
returned_job = ml_client.jobs.create(classification_job)
ml_client.jobs.stream(returned_job.name)

Monitor the Training Process

AutoML will automatically try different models and configurations. You can monitor the progress and see the best-performing models in the Azure Machine Learning studio under the "Jobs" section.

Key metrics to observe include accuracy, precision, recall, F1-score, and AUC.
Evaluate and Deploy the Best Model

Once the job completes, AutoML will identify the best model. You can then register this model and deploy it as a web service (e.g., a REST API endpoint) for real-time predictions or batch inferencing.

Deployment can be done directly from the Azure ML studio or programmatically using the SDK.

Advanced Options

Feature Engineering: AutoML can automatically perform feature engineering, but you can also provide custom features.
Algorithm Customization: While AutoML automates algorithm selection, you can specify a list of allowed or blocked algorithms.
Model Interpretation: Understand which features are most important for your model's predictions using built-in interpretability tools.

Learn More

For detailed information and more advanced scenarios, refer to the official Azure Machine Learning documentation: