Precision

• Precision Score is a metric used to evaluate the Accuracy of a Classification model, specifically focusing on the positive class.
• It is defined as the proportion of true positive predictions among all positive predictions made by the model.
• Classification Report & Precision or Recall
• The formula for precision is: $\text{Precision}=\frac{TP}{TP+FP}$ where:
  • TP (True Positives): The number of correctly predicted positive instances.
  • FP (False Positives): The number of instances incorrectly predicted as positive.

Importance:

• Precision is crucial in scenarios where the cost of false positives is high, such as in spam detection or medical diagnosis.
• It helps in understanding how many of the predicted positive instances are actually positive.

Implementing Precision Score in Python

You can calculate the precision score in Python using the scikit-learn library, which provides a convenient function for this purpose.

Example Usage

from sklearn.metrics import precision_score
 
# Sample true labels and predicted labels
y_true = [0, 1, 1, 0, 1, 0, 1, 1]
y_pred = [0, 1, 0, 0, 1, 1, 1, 1]
 
# Calculate precision score
precision = precision_score(y_true, y_pred)
 
print("Precision Score: {:.2f}".format(precision))

• The precision_score function can handle binary and multiclass classification problems. For multiclass, you can specify the average parameter to determine how to calculate the precision (e.g., average='macro', average='micro', or average='weighted').