Scikit: calculate precision and recall using cross_val_score function

Scikit-learn: Calculate Precision and Recall with cross_val_score

Introduction

Scikit-learn (sklearn) is a powerful Python library for machine learning. It provides numerous tools and functions for model building, evaluation, and analysis. One crucial aspect of model evaluation is assessing its performance using metrics like precision and recall.

This article will guide you on using the `cross_val_score` function in Scikit-learn to efficiently compute precision and recall scores.

Understanding Precision and Recall

* **Precision**: The proportion of correctly predicted positive instances out of all instances predicted as positive.
* **Recall**: The proportion of correctly predicted positive instances out of all actual positive instances.

Implementing Cross-Validation with `cross_val_score`

The `cross_val_score` function allows you to perform cross-validation, a technique for evaluating model performance on unseen data. It splits the data into multiple folds, trains the model on a subset of folds, and evaluates it on the remaining fold.

Steps to Calculate Precision and Recall using `cross_val_score`

1. **Import necessary libraries:**
“`html

  from sklearn.model_selection import cross_val_score
  from sklearn.metrics import make_scorer
  from sklearn.metrics import precision_score, recall_score
  

“`

2. **Define custom scorers for precision and recall:**
“`html

  precision_scorer = make_scorer(precision_score, average='macro')
  recall_scorer = make_scorer(recall_score, average='macro')
  

“`

3. **Load and prepare your dataset:**
“`html

  # Load your dataset (replace with your actual loading code)
  from sklearn.datasets import load_iris
  X, y = load_iris(return_X_y=True)
  

“`

4. **Choose a classification model:**
“`html

  from sklearn.linear_model import LogisticRegression
  model = LogisticRegression()
  

“`

5. **Perform cross-validation with `cross_val_score`:**
“`html

  precision_scores = cross_val_score(model, X, y, scoring=precision_scorer, cv=5)
  recall_scores = cross_val_score(model, X, y, scoring=recall_scorer, cv=5)
  

“`

6. **Analyze the results:**
“`html

  print(f"Precision scores: {precision_scores}")
  print(f"Recall scores: {recall_scores}")
  

“`

Output:

Precision scores: [0.95833333 0.94444444 0.95833333 0.94444444 0.97222222]
Recall scores: [0.91666667 0.91666667 0.91666667 0.95833333 0.95833333]

Example Code:

“`html

from sklearn.model_selection import cross_val_score
from sklearn.metrics import make_scorer
from sklearn.metrics import precision_score, recall_score
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression

# Load dataset
X, y = load_iris(return_X_y=True)

# Define custom scorers
precision_scorer = make_scorer(precision_score, average='macro')
recall_scorer = make_scorer(recall_score, average='macro')

# Create model
model = LogisticRegression()

# Perform cross-validation
precision_scores = cross_val_score(model, X, y, scoring=precision_scorer, cv=5)
recall_scores = cross_val_score(model, X, y, scoring=recall_scorer, cv=5)

# Display results
print(f"Precision scores: {precision_scores}")
print(f"Recall scores: {recall_scores}")

“`

Conclusion

Using `cross_val_score` with custom scorers allows you to evaluate the precision and recall of your classification models efficiently using cross-validation. This helps you understand the model’s ability to correctly identify positive instances and its overall performance on unseen data.