Question

我在一个示例乳腺癌数据集上训练了基本的FFNN。对于结果，precision_recall_curve函数为416个不同阈值提供数据点。据我了解，“精确调用曲线”可以包含569个唯一的预测值，我可以应用568个不同的阈值并检查生成的“精确调用”。

但是我该怎么做呢？有没有办法设置要使用sklearn测试的阈值数量？还是至少对sklearn如何选择这些阈值的解释？

我的意思是417应该足够了，即使对于更大的数据集，我也很好奇它们是如何被选择的。

# necessary packages
from sklearn.datasets import load_breast_cancer
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout

# load data
sk_data = load_breast_cancer(return_X_y=False)

# safe data in pandas
data = sk_data['data']
target = sk_data['target']
target_names = sk_data['target_names']
feature_names = sk_data['feature_names']
data = pd.DataFrame(data=data, columns=feature_names)

# build ANN
model = Sequential()
model.add(Dense(64, kernel_initializer='random_uniform', input_dim=30, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(32, kernel_initializer='random_uniform', activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(1, activation='sigmoid'))

# train ANN
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()

model.fit(data, target, epochs=50, batch_size=10, validation_split=0.2)

# eval
pred = model.predict(data)

# calculate precision-recall curve
from sklearn.metrics import precision_recall_curve
precision, recall, thresholds = precision_recall_curve(target, pred)

# precision-recall curve and f1
import matplotlib.pyplot as plt

#pyplot.plot([0, 1], [0.5, 0.5], linestyle='--')
plt.plot(recall, precision, marker='.')
# show the plot
plt.show()

len(np.unique(pred)) #569
len(thresholds) # 417

Answer 1

读取source，precision_recall_curve会针对每个唯一的预测概率（此处为pred）计算精度和召回率，但是会忽略所有导致完全召回的阈值的输出（除了实现完全召回的第一个门槛）。

sklearn如何选择精度召回曲线中的阈值步长？

1 个答案: