When using in machine learning, two metrics we often use to assess the quality of the model are precision and recall.

Precision: Correct positive predictions relative to total positive predictions.

This is calculated as:

• Precision = True Positives / (True Positives + False Positives)

Recall: Correct positive predictions relative to total actual positives

This is calculated as:

• Recall = True Positives / (True Positives + False Negatives)

To visualize the precision and recall for a certain model, we can create a precision-recall curve. This curve shows the tradeoff between precision and recall for different thresholds.

The following step-by-step example shows how to create a precision-recall curve for a logistic regression model in Python.

Step 1: Import Packages

First, we’ll import the necessary packages:

from sklearn import datasets
from sklearn.model_selectionimport train_test_split
from sklearn.linear_modelimport LogisticRegression
from sklearn.metricsimport precision_recall_curve
import matplotlib.pyplotas plt

Step 2: Fit the Logistic Regression Model

Next, we’ll create a dataset and fit a logistic regression model to it:

#create dataset with 5 predictor variables
X, y = datasets.make_classification(n_samples=1000,
                                    n_features=4,
                                    n_informative=3,
                                    n_redundant=1,
                                    random_state=0)

#split dataset into training and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3,random_state=0)

#fit logistic regression model to dataset
classifier = LogisticRegression()
classifier.fit(X_train, y_train)

#use logistic regression model to make predictions
y_score = classifier.predict_proba(X_test)[:, 1]

Step 3: Create the Precision-Recall Curve

#calculate precision and recall
precision, recall, thresholds = precision_recall_curve(y_test, y_score)

#create precision recall curve
fig, ax = plt.subplots()
ax.plot(recall, precision, color='purple')

#add axis labels to plot
ax.set_title('Precision-Recall Curve')
ax.set_ylabel('Precision')
ax.set_xlabel('Recall')

#display plot
plt.show()

The x-axis shows the recall and the y-axis shows the precision for various thresholds.

Notice that as recall increases, precision decreases.

This represents the tradeoff between the two metrics. To increase the recall of our model, the precision must decrease and vice versa.