If you’re a business analyst, grasping customer behavior is likely among your top priorities. Understanding the factors influencing customer purchase decisions can significantly boost revenue. However, customer churn—the loss of customers—remains a constant threat. Insights into why customers leave are essential for maintaining profits and revenue.
While machine learning (ML) offers valuable insights, developing customer churn prediction models typically required ML expertise until the arrival of Amazon SageMaker Canvas. SageMaker Canvas is a low-code/no-code managed service that empowers users to create ML models addressing various business challenges without any coding. It also allows users to evaluate these models with advanced metrics, similar to a data scientist.
In this article, we illustrate how a business analyst can assess and interpret a classification churn model developed with SageMaker Canvas, utilizing the Advanced metrics tab. We will clarify the metrics and share techniques for enhancing model performance.
Prerequisites
To execute the tasks outlined in this article, you’ll need an AWS account with access to SageMaker Canvas. For foundational information regarding SageMaker Canvas, the churn model, and the dataset, refer to this blog post.
Understanding Model Performance Evaluation
When evaluating a model’s performance, the goal is to measure how accurately the model can predict outcomes based on unseen data, often referred to as inference. You begin by training the model using existing data and then assess its predictions on new data. The accuracy of these predictions helps gauge model performance.
To ensure reliable evaluation, historical data with known outcomes is required. This is accomplished by reserving a subset of historical training data to compare against the model’s predictions.
In the case of customer churn, which is a categorical classification task, you start with a historical dataset containing various customer attributes. One attribute, called Churn, indicates whether a customer has left (True) or stayed (False). To evaluate model accuracy, the dataset is divided into two parts: a training dataset for model training and a test dataset for making predictions, which are then compared to the actual values in the test dataset.
Interpreting Advanced Metrics
This section delves into the advanced metrics available in SageMaker Canvas that can provide insights into model performance.
Confusion Matrix
SageMaker Canvas employs confusion matrices to help visualize the model’s prediction accuracy. A confusion matrix organizes results to juxtapose predicted values against actual historical values. In a binary prediction model, the confusion matrix comprises:
- True Positive: Correctly predicted positive outcomes.
- True Negative: Correctly predicted negative outcomes.
- False Positive: Incorrectly predicted positive outcomes.
- False Negative: Incorrectly predicted negative outcomes.
The confusion matrix for our churn model uses actual values from the test dataset and predictions generated by the model.
Accuracy
Accuracy is the ratio of correct predictions to the total number of samples in the test set. It combines true positives and true negatives, divided by the overall number of samples. While accuracy is a key metric, it can be misleading in specific scenarios. For instance:
- Class Imbalance: When dataset classes are unevenly distributed, a model may achieve high accuracy simply by predicting the majority class.
- Cost-Sensitive Classification: In certain applications, misclassification costs vary between classes. For example, predicting whether a medication exacerbates a condition can entail different consequences for false positives versus false negatives.
Precision, Recall, and F1 Score
Precision measures the proportion of true positives among all predicted positives, while recall assesses the proportion of true positives among all actual positives. The F1 score, the harmonic mean of precision and recall, balances both metrics and ranges from 0 to 1, with a higher score indicating better performance. A perfect F1 score signifies flawless precision and recall, while a score of zero indicates completely inaccurate predictions.
The F1 score is particularly relevant in contexts such as medical diagnosis and fraud detection, where accurate identification and minimizing false results are critical. To learn more about the importance of these metrics, check out this authoritative source.
AUC (Area Under the Curve)
The AUC metric evaluates a model’s ability to distinguish between classes, providing a comprehensive view of its performance.
For additional insights, visit this excellent resource for a community discussion on onboarding and best practices.
In conclusion, understanding these advanced metrics allows business analysts to effectively evaluate and enhance their ML models, ultimately leading to better predictions and informed business decisions.
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