Model Confusion: Insights from the Confusion Matrix

Let's examine which categories our model classified correctly and where it stumbled. We'll create a confusion matrix—and yes, that's genuinely one of the most aptly named tools in data science.

A confusion matrix reveals exactly where our model became confused, providing a granular view of classification errors across different categories. While we might have our bearings straight, our model clearly encountered some challenges. Despite achieving that 77% overall accuracy, you'll discover that certain categories proved particularly problematic for our algorithm.

The confusion matrix will expose a critical weakness: our model's struggle with specific prediction scenarios that significantly impact its real-world utility. This granular analysis is essential for understanding model performance beyond simple accuracy metrics.

We'll create our confusion matrix using the standard approach. First, we'll initialize CM—the conventional variable name for confusion matrix—using the confusion_matrix function from sklearn.metrics. This function requires two key inputs: the actual correct labels from our test dataset and our model's predictions. Then we'll transform this raw matrix into a more readable pandas DataFrame for easier interpretation.

Let's structure this data for maximum clarity. We'll create CMDF, our confusion matrix DataFrame, passing the confusion matrix as the underlying data. The columns will be labeled "Predicted Stayed" and "Predicted Left," while the row indices will show "Actually Stayed" and "Actually Left." This four-quadrant view provides immediate insight into our model's decision-making patterns.

Now, examining our results reveals a mixed performance picture. Our model demonstrated strong capabilities in certain areas while exposing significant blind spots in others.

The numbers tell a compelling story. We correctly predicted 8,538 employees would stay—and they did. We also accurately identified 734 departures. These diagonal entries (upper-left and lower-right) represent our model's successes: predicted stayed/actually stayed and predicted left/actually left.

For employees we predicted would stay, our accuracy rate sits at approximately 80%—a respectable performance indicating strong retention prediction capabilities. However, the story becomes more complex when we examine departure predictions.

Among employees we predicted would leave, our accuracy drops to roughly 55-60%. This represents a concerning decline in predictive power for this critical business scenario. More troubling still is what happens when we flip the perspective to examine actual departures.

Here's where our model's limitations become starkly apparent. Of the approximately 2,800 employees who actually left the company, we correctly identified only about 25%—a disappointing result with serious business implications. This means our model missed three-quarters of actual departures, predicting these employees would stay when they were actually planning to leave.

The magnitude of this misclassification is striking: roughly 2,100 employees who actually departed were incorrectly flagged as likely to stay, compared to only 700 correctly identified departures. For organizations relying on predictive models for workforce planning, retention strategies, or succession planning, this level of false negatives could prove costly. Missing early departure signals means missed opportunities for intervention, retention efforts, and knowledge transfer—all critical factors in maintaining organizational stability and reducing turnover costs.