python使用sklearn的PrecisionRecallDisplay来可视化PR曲线

目录

python使用sklearn的PrecisionRecallDisplay来可视化PR曲线

#模型构建

#PrecisionRecallDisplay来可视化PR曲线

#模型构建

print(__doc__)
from sklearn.datasets import fetch_openml
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

X, y = fetch_openml(data_id=1464, return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y)

clf = make_pipeline(StandardScaler(), LogisticRegression(random_state=0))
clf.fit(X_train, y_train)

#PrecisionRecallDisplay来可视化PR曲线

from sklearn.metrics import precision_recall_curve
from sklearn.metrics import PrecisionRecallDisplay

prec, recall, _ = precision_recall_curve(y_test, y_score,
                                         pos_label=clf.classes_[1])
pr_display = PrecisionRecallDisplay(precision=prec, recall=recall).plot()

参考：RocCurveDisplay

参考：受试者工作特征曲线 (receiver operating characteristic curve,简称ROC曲线)