Now let's apply the same rigorous evaluation methodology we used for our linear regression model to assess the performance of our classification algorithm. This comparative analysis will reveal how effectively our model distinguishes between employees who stay versus those who leave the organization.
First, we'll generate predictions on our test dataset using our trained model. I'll store these results as `predictions` for subsequent analysis. Given that we're working with approximately 3,000 test cases, we'll examine representative samples rather than overwhelming ourselves with the complete dataset output.
Let's start by comparing the first 20 actual outcomes with our model's predictions. We'll convert both Y_test and our predictions to lists and examine these initial cases to get an immediate sense of our model's accuracy.
The results reveal an interesting pattern. While the predictions aren't perfectly aligned with reality, our model demonstrates strong performance overall. The zeros represent employees who remained with the company, while ones indicate departures. In this first sample, we can identify specific misclassifications: the third employee actually left but our model predicted they would stay, and conversely, the first employee remained but we predicted departure.
This gives us two incorrect predictions out of 20 cases—a 90% accuracy rate for this sample. However, let's expand our analysis to ensure we're not drawing conclusions from a potentially favorable subset of predictions.
Examining predictions 20 through 40 reveals more challenging cases where our model's performance varies. In this second batch, we identified five incorrect predictions: two employees we predicted would stay actually left, and three departures went completely undetected by our algorithm.
This translates to five errors out of 20 predictions, yielding a 75% accuracy rate for this particular subset. The variance between these small samples underscores why we need comprehensive evaluation metrics rather than relying on limited anecdotal evidence from tiny subsets of our 3,000-case test dataset.
For a definitive assessment, let's calculate our model's overall accuracy score. Unlike regression metrics that measure proximity to target values, classification accuracy simply measures the percentage of correct binary predictions—a straightforward but crucial performance indicator.
Using our model's built-in scoring function with the complete test dataset and corresponding ground truth labels, we achieve an overall accuracy of 77%. This represents solid performance for employee retention prediction, a notoriously complex classification challenge involving numerous human factors and organizational variables.
While 77% accuracy provides a strong foundation, the real insights emerge when we analyze the specific types of errors our model makes. Understanding these patterns will help us identify potential biases and areas for improvement in future iterations.
Let's categorize our predictions using the standard classification framework. When our model correctly predicts an employee will stay (predicting 0 when the actual outcome is 0), we have a **true negative**. When we correctly predict departure (predicting 1 when the actual outcome is 1), that's a **true positive**. These represent our successful predictions.
However, our misclassifications fall into two distinct categories, each with different business implications. A **false negative** occurs when we predict an employee will stay (0) but they actually leave (1). This type of error means we failed to identify at-risk employees who subsequently departed—potentially missing opportunities for retention interventions.
Conversely, a **false positive** happens when we predict departure (1) but the employee actually stays (0). While less operationally disruptive than false negatives, these errors could lead to unnecessary retention efforts or misallocated resources.
The distinction between these error types is crucial for HR strategy. False negatives represent missed opportunities to retain valuable talent, while false positives might result in over-investing in retention efforts for employees who weren't actually at risk. In the next section, we'll dive deeper into advanced evaluation metrics that illuminate these nuanced performance characteristics and guide our model optimization efforts.