Evaluating the accuracy of a machine learning (ML) model is a crucial step to determine its performance and effectiveness. The choice of evaluation metrics depends on the type of problem you’re solving (classification, regression, etc.), and it’s important to select metrics that are relevant to your specific goals. Here’s a general process for assessing the accuracy of an ML model.
Mean Squared Error (MSE) measures the average squared difference between the predicted values and the actual values. It gives higher weight to larger errors.
Formula: \(MSE = (1/n) * Σ(y_a - y_p)^2\)
mse = mean_squared_error(actual_values, predicted_values) print("Mean Squared Error:", mse)
Lower MSE values indicate better model performance. It’s important to note that while MSE provides a useful measure of prediction accuracy, it emphasizes larger errors due to squaring them. This can make it sensitive to outliers, potentially leading to overemphasizing the impact of extreme values. As a result, it’s recommended to consider MSE alongside other evaluation metrics and to interpret it in the context of the specific problem and dataset being analyzed.
Accuracy score is a metric for classification accuracy, which calculates the proportion of correctly predicted instances out of the total instances in the dataset.
accuracy = accuracy_score(actual_labels, predicted_labels) print("Accuracy:", accuracy)
It’s important to keep in mind that accuracy alone might not provide a complete picture, especially in scenarios where the costs of different types of errors vary (More details in When Not to Use ML). Therefore, using additional evaluation metrics can offer a more comprehensive assessment of the model’s classification performance.