precision recall curve

from sklearn.metrics import precision_recall_curve, average_precision_score
import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.neural_network import MLPClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

#Dataset
cancer_dataset = load_breast_cancer()
X=cancer_dataset.data
y=cancer_dataset.target

#Model Implementation
mlp = MLPClassifier(max_iter=500)

# Split data into 50% train and 50% test subsets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, shuffle=False)

# Learn the digits on the train subset
mlp.fit(X_train, y_train)

# Predict the value of the digit on the test subset
predicted = mlp.predict(X_test)


# Calculate precision-recall curve
precision, recall, thresholds = precision_recall_curve(y_test, predicted)

# Calculate average precision
average_precision = average_precision_score(y_test, predicted)

# Plot precision-recall curve
plt.step(recall, precision, where="post")
plt.xlabel("Recall")
plt.ylabel("Precision")
plt.title("Precision-Recall Curve (AP={:.2f})".format(average_precision))
plt.show()