# Assessing Accuracy#

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 youâ€™re solving (classification, regression, etc.), and itâ€™s important to select metrics that are relevant to your specific goals. Hereâ€™s a general process for assessing the accuracy of an ML model.

## Regression#

**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: \(MSE = (1/n) * Î£(y_a - y_p)^2\)**

```
mse = mean_squared_error(actual_values, predicted_values)
print("Mean Squared Error:", mse)
```

Lower MSE values indicate better model performance. Itâ€™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, itâ€™s recommended to consider MSE alongside other evaluation metrics and to interpret it in the context of the specific problem and dataset being analyzed.

## Classification#

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)
```

Itâ€™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 modelâ€™s classification performance.