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Fundamentals of ML observability
Metrics, feature importance and more
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Today, we’ll take a look at the intricacies of the machine learning evaluation metric, Precision. In this practical guide, we aim to equip you with the knowledge of how precision is calculated, its relevance, its trade-offs, and how to use it to evaluate ML performance.
Precision is one of the cornerstone metrics used in the evaluation of machine learning models, particularly in classification problems. It’s crucial in scenarios where the cost of False Positives is high.
Essentially, Precision quantifies the proportion of true positives (TP) over the sum of true positives and false positives (FP). It is mathematically expressed as:
Precision = TP / (TP + FP)
For example, consider a model designed to predict if a bank transaction is fraudulent. In this case, a false positive would mean an ordinary transaction being flagged as fraudulent—an instance we’d like to minimize. Here, precision plays a crucial role.
def calculate_precision(predictions, actuals):
if len(predictions) != len(actuals):
raise ValueError("The length of predictions and actuals lists must be equal.")
true_positives = sum([p == a == 1 for p, a in zip(predictions, actuals)])
false_positives = sum([p == 1 and a == 0 for p, a in zip(predictions, actuals)])
if true_positives + false_positives == 0:
precision = true_positives / (true_positives + false_positives)
predictions = [1, 0, 1, 1, 0, 1, 0, 0, 1, 1] # Sample prediction values
actuals = [1, 0, 0, 1, 0, 1, 1, 0, 1, 0] # Sample actual values
Note: In this example, predictions and actuals are lists with binary values, where 1 represents a fraudulent transaction and 0 represents a normal transaction.
Also, this is a simple way to calculate precision and doesn’t consider many nuances and edge cases that might be involved in a real-world scenario. For a production-level code, you would probably use an established library, such as scikit-learn, which has built-in functions to compute precision, recall, and many other metrics.
Precision is often used alongside other metrics, such as recall (also known as sensitivity) and accuracy.
To illustrate the difference between precision and recall, let’s use a practical example with code. We will use the sklearn library to calculate these metrics:
from sklearn.metrics import precision_score, recall_score
# Actual values
y_true = [0, 1, 1, 0, 1, 1, 0, 0, 0, 1]
# Predicted values
y_pred = [0, 0, 1, 1, 1, 1, 0, 1, 0, 0]
precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred)
In this example, you’ll notice the difference between precision and recall. While precision provides the accuracy of positive predictions, recall informs us about the fraction of positives our model can identify correctly.
Precision holds a significant role in model monitoring and evaluation. It provides an essential measure of your model’s performance, particularly in applications where false positives have substantial consequences.
For instance, in fraud detection, falsely classifying a true transaction as fraudulent (false positive) is more consequential than letting a fraudulent payment slip through the cracks (false negative). Precision helps monitor these false positives effectively, allowing for the refinement of the model’s performance over time.
However, precision alone doesn’t provide the complete picture. It must be balanced with other metrics like recall, especially when the cost of missing a positive instance (false negative) is high.
While precision is a helpful metric, it has its trade-offs.
Understanding precision, its merits, and trade-offs is crucial when evaluating machine learning models. While precision might not always be the best standalone metric, it’s often used in conjunction with others to provide a holistic view of model performance.
Always remember, the choice of metric should align with the problem at hand and the cost associated with the type of error. In the next posts, we will cover other important metrics in detail, ensuring you’re well-versed in all facets of ML model evaluation. Stay tuned!Reach out to learn more about model monitoring.