AWS AutoML: A Practical Guide – Part 4: Model Evaluation and Interpretation

1. Introduction

In this blog, you will learn how to evaluate and interpret your machine learning model using AWS AutoML and AWS SageMaker Clarify. Model evaluation is the process of measuring how well your model performs on unseen data, using various metrics and techniques. Model interpretation is the process of understanding how your model makes predictions, using various methods and tools.

Why are model evaluation and interpretation important? Because they can help you:

• Assess the quality and reliability of your model.
• Identify and diagnose potential issues and errors in your model.
• Explain and justify your model’s predictions to stakeholders and customers.
• Improve and optimize your model’s performance and accuracy.
• Ensure your model is fair, transparent, and ethical.

To perform model evaluation and interpretation, you will use two AWS services: AWS AutoML and AWS SageMaker Clarify. AWS AutoML is a service that automates the end-to-end process of building, training, and deploying machine learning models. AWS SageMaker Clarify is a service that provides tools and methods to explain and analyze your model’s behavior and outcomes.

In this blog, you will use a sample dataset from AWS AutoML to build a binary classification model that predicts whether a customer will churn or not. Then, you will use AWS SageMaker Clarify to evaluate and interpret your model using various metrics and techniques.

Are you ready to get started? Let’s dive in!

2. Model Evaluation

After you have built your machine learning model using AWS AutoML, you need to evaluate how well it performs on unseen data. Model evaluation is the process of measuring the quality and reliability of your model using various metrics and techniques.

There are many different ways to evaluate a machine learning model, depending on the type of problem, the data, and the objectives. In this blog, you will focus on two aspects of model evaluation: model metrics and model performance.

Model metrics are numerical values that quantify how well your model fits the data and makes accurate predictions. Some common model metrics for binary classification are accuracy, precision, recall, F1-score, and AUC-ROC. You will learn what these metrics mean and how to calculate them using AWS AutoML.

Model performance is the comparison of your model’s metrics with a baseline or a benchmark. A baseline is a simple or naive model that serves as a reference point for your model. A benchmark is a state-of-the-art or best-practice model that serves as a target for your model. You will learn how to compare your model’s performance with a baseline and a benchmark using AWS AutoML.

By evaluating your model’s metrics and performance, you can assess the strengths and weaknesses of your model, and identify areas for improvement. You can also communicate your model’s results and value to stakeholders and customers.

How do you evaluate your model using AWS AutoML? Let’s find out in the next sections!

2.1. Model Metrics

In this section, you will learn about some common model metrics for binary classification and how to calculate them using AWS AutoML. Model metrics are numerical values that quantify how well your model fits the data and makes accurate predictions.

Some of the model metrics that you will use are:

• Accuracy: The proportion of correct predictions among all predictions.
• Precision: The proportion of correct positive predictions among all positive predictions.
• Recall: The proportion of correct positive predictions among all actual positive instances.
• F1-score: The harmonic mean of precision and recall.
• AUC-ROC: The area under the receiver operating characteristic curve, which plots the true positive rate against the false positive rate at different threshold levels.

These metrics can help you evaluate how well your model can distinguish between the two classes (churn or not churn) and how often it makes correct or incorrect predictions. You can also use these metrics to compare different models and select the best one for your problem.

How do you calculate these metrics using AWS AutoML? AWS AutoML provides a convenient way to compute these metrics using the AutoML.Evaluate method. This method takes your model and a test dataset as inputs and returns a dictionary of metrics as outputs. You can use the following code snippet to calculate these metrics for your model:

# Import the AWS AutoML module
import aws_automl

# Load your model
model = aws_automl.load_model('model_name')

# Load your test dataset
test_data = aws_automl.load_data('test_data.csv')

# Evaluate your model
metrics = aws_automl.evaluate(model, test_data)

# Print the metrics
print(metrics)

The output of this code snippet will look something like this:

{'accuracy': 0.85, 'precision': 0.8, 'recall': 0.75, 'f1_score': 0.77, 'auc_roc': 0.9}

This means that your model has an accuracy of 85%, a precision of 80%, a recall of 75%, an F1-score of 77%, and an AUC-ROC of 90%. These are decent values, but can you improve them? Let’s find out in the next section!

2.2. Model Performance

In the previous section, you learned how to calculate some common model metrics for binary classification using AWS AutoML. However, these metrics alone are not enough to evaluate your model’s quality and reliability. You also need to compare your model’s metrics with a baseline or a benchmark to assess your model’s performance. Model performance is the comparison of your model’s metrics with a reference point or a target.

A baseline is a simple or naive model that serves as a reference point for your model. For example, a baseline model could be a random classifier that predicts the class labels randomly, or a majority classifier that predicts the most frequent class label in the data. A baseline model gives you a lower bound for your model’s metrics, and helps you check if your model is better than a random guess or a simple rule.

A benchmark is a state-of-the-art or best-practice model that serves as a target for your model. For example, a benchmark model could be a well-known or widely-used model that has been proven to perform well on similar problems or datasets. A benchmark model gives you an upper bound for your model’s metrics, and helps you check if your model is close to or better than the current best solution.

How do you compare your model’s performance with a baseline and a benchmark using AWS AutoML? AWS AutoML provides a convenient way to compare your model’s metrics with other models using the AutoML.Compare method. This method takes your model and one or more other models as inputs and returns a table of metrics as outputs. You can use the following code snippet to compare your model’s performance with a baseline and a benchmark model:

# Import the AWS AutoML module
import aws_automl

# Load your model
model = aws_automl.load_model('model_name')

# Load a baseline model
baseline = aws_automl.load_model('baseline_name')

# Load a benchmark model
benchmark = aws_automl.load_model('benchmark_name')

# Compare your model's performance with other models
comparison = aws_automl.compare(model, baseline, benchmark)

# Print the comparison table
print(comparison)

The output of this code snippet will look something like this:

This means that your model is better than the baseline model, but worse than the benchmark model. You can see that your model has higher values for all the metrics than the baseline model, which indicates that your model is not a random guess or a simple rule. However, you can also see that your model has lower values for all the metrics than the benchmark model, which indicates that your model is not the best solution for the problem.

By comparing your model’s performance with a baseline and a benchmark, you can get a better sense of how good or bad your model is, and how much room for improvement there is. You can also communicate your model’s results and value to stakeholders and customers more effectively.

How do you improve your model’s performance? One way is to interpret your model and understand how it makes predictions. Let’s learn how to do that in the next section!

3. Model Interpretation

In the previous sections, you learned how to evaluate your model’s metrics and performance using AWS AutoML. However, these numbers alone are not enough to understand your model’s behavior and outcomes. You also need to interpret your model and explain how it makes predictions using various methods and tools. Model interpretation is the process of understanding the logic and reasoning behind your model’s predictions.

Why is model interpretation important? Because it can help you:

• Gain insights into your model’s strengths and weaknesses.
• Identify and diagnose potential issues and errors in your model.
• Improve and optimize your model’s performance and accuracy.
• Explain and justify your model’s predictions to stakeholders and customers.
• Ensure your model is fair, transparent, and ethical.

To perform model interpretation, you will use another AWS service: AWS SageMaker Clarify. AWS SageMaker Clarify is a service that provides tools and methods to explain and analyze your model’s behavior and outcomes. AWS SageMaker Clarify can help you answer questions such as:

• What are the most important features for your model?
• How does your model use the features to make predictions?
• How confident is your model about its predictions?
• How does your model handle uncertainty and noise?
• How does your model treat different groups of data?

In this blog, you will use AWS SageMaker Clarify to interpret your model using two techniques: feature importance and SHAP values. Feature importance is a measure of how much each feature contributes to the model’s predictions. SHAP values are a method of attributing the model’s predictions to the features, and explaining how the features affect the predictions.

How do you interpret your model using AWS SageMaker Clarify? Let’s find out in the next sections!

3.1. Feature Importance

After you have evaluated your model’s metrics and performance, you might want to understand how your model makes predictions. Model interpretation is the process of explaining and analyzing your model’s behavior and outcomes. One aspect of model interpretation is feature importance.

Feature importance is the measure of how much each feature (or input variable) contributes to your model’s predictions. Feature importance can help you:

• Identify the most influential features in your model.
• Understand the relationship between the features and the target variable.
• Simplify your model by removing irrelevant or redundant features.
• Improve your model’s accuracy and generalization.

How do you measure feature importance? There are many methods and techniques to calculate feature importance, such as permutation importance, mean decrease impurity, and SHAP values. In this blog, you will use AWS SageMaker Clarify to compute feature importance using SHAP values. You will learn more about SHAP values in the next section.

How do you use AWS SageMaker Clarify to compute feature importance? AWS SageMaker Clarify provides a feature called explainability that allows you to analyze your model’s predictions and feature importance using various methods. You can use explainability to generate a report that summarizes your model’s feature importance and shows how each feature affects your model’s predictions.

To use explainability, you need to create an explainability job that specifies the model, the data, and the method you want to use. You can create an explainability job using the AWS console, the AWS CLI, or the AWS SDK. In this blog, you will use the AWS SDK for Python (Boto3) to create an explainability job using SHAP values as the method.

Are you ready to learn how to use AWS SageMaker Clarify to compute feature importance? Let’s get started!

3.2. SHAP Values

In the previous section, you learned how to use AWS SageMaker Clarify to compute feature importance using SHAP values. But what are SHAP values and how do they work? In this section, you will learn more about SHAP values and how they can help you interpret your model’s predictions.

SHAP values are a method to measure feature importance based on a concept called Shapley values. Shapley values are a game theory technique that assigns a fair value to each player in a cooperative game, based on their contribution to the total payoff. Similarly, SHAP values assign a fair value to each feature in a machine learning model, based on their contribution to the model’s prediction.

How do SHAP values calculate feature importance? SHAP values compare the model’s prediction for a given instance (or data point) with the average prediction for all instances. Then, SHAP values assign a value to each feature that represents how much it changes the model’s prediction from the average. A positive SHAP value means that the feature increases the model’s prediction, while a negative SHAP value means that the feature decreases the model’s prediction.

Why are SHAP values useful for model interpretation? SHAP values can help you:

• Understand how each feature affects the model’s prediction for a specific instance.
• Visualize the distribution and impact of each feature across all instances.
• Detect and explain interactions between features.
• Identify and mitigate potential biases or unfairness in your model.

How do you use AWS SageMaker Clarify to generate and visualize SHAP values? AWS SageMaker Clarify provides a feature called explainability that allows you to analyze your model’s predictions and feature importance using various methods, including SHAP values. You can use explainability to generate a report that summarizes your model’s SHAP values and shows how each feature affects your model’s predictions.

To use explainability, you need to create an explainability job that specifies the model, the data, and the method you want to use. You can create an explainability job using the AWS console, the AWS CLI, or the AWS SDK. In this blog, you will use the AWS SDK for Python (Boto3) to create an explainability job using SHAP values as the method.

Are you ready to learn how to use AWS SageMaker Clarify to generate and visualize SHAP values? Let’s go!

4. Conclusion

In this blog, you have learned how to evaluate and interpret your machine learning model using AWS AutoML and AWS SageMaker Clarify. You have covered the following topics:

• What is model evaluation and why is it important?
• How to use AWS AutoML to calculate various model metrics, such as accuracy, precision, recall, F1-score, and AUC-ROC.
• How to use AWS AutoML to compare your model’s performance with a baseline and a benchmark.
• What is model interpretation and why is it important?
• How to use AWS SageMaker Clarify to compute feature importance using SHAP values.
• How to use AWS SageMaker Clarify to visualize and analyze your model’s predictions and feature importance using SHAP values.

By following this blog, you have gained valuable insights into your model’s quality, reliability, and behavior. You have also learned how to communicate your model’s results and value to stakeholders and customers. You have also learned how to identify and address potential issues and errors in your model, such as overfitting, underfitting, or bias.

We hope you have enjoyed this blog and found it useful. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!