January 2018
Intermediate to advanced
301 pages
8h 54m
Chinese
Content preview from Python机器学习基础教程
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5
章
In[54]:
from sklearn.metrics import precision_recall_curve
precision, recall, thresholds = precision_recall_curve(
y_test, svc.decision_function(X_test))
precision_recall_curve 函数返回一个列表，包含按顺序排序的所有可能阈值（在决策
函数中出现的所有值）对应的准确率和召回率，这样我们就可以绘制一条曲线，如图
5-13
所示：
In[55]:
# 使用更多数据点来得到更加平滑的曲线
X, y = make_blobs(n_samples=(4000, 500), centers=2, cluster_std=[7.0, 2],
random_state=22)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
svc = SVC(gamma=.05).fit(X_train, y_train)
precision, recall, thresholds = precision_recall_curve(
y_test, svc.decision_function(X_test))
# 找到最接近于0的阈值
close_zero = np.argmin(np.abs(thresholds))
plt.plot(precision[close_zero], ...
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