分类的性能评估：准确率、精确率、Recall召回率、F1、F2

import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt

df = pd.read_csv('./sms.csv')
X_train_raw, X_test_raw, y_train, y_test = train_test_split(df['message'], df['label'], random_state=11)
vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(X_train_raw)
X_test = vectorizer.transform(X_test_raw)
classifier = LogisticRegression()
classifier.fit(X_train, y_train)
scores = cross_val_score(classifier, X_train, y_train, cv=5)
print('Accuracies: %s' % scores)
print('Mean accuracy: %s' % np.mean(scores))

Accuracies: [ 0.95221027  0.95454545  0.96172249  0.96052632  0.95209581]
Mean accuracy: 0.956220068309

precisions = cross_val_score(classifier, X_train, y_train, cv=5, scoring='precision')
print('Precision: %s' % np.mean(precisions))
recalls = cross_val_score(classifier, X_train, y_train, cv=5, scoring='recall')
print('Recall: %s' % np.mean(recalls))
f1s = cross_val_score(classifier, X_train, y_train, cv=5, scoring='f1')
print('F1 score: %s' % np.mean(f1s))

Precision: 0.992542742398
Recall: 0.683605030275

F1 score: 0.809067846627
F1是精确率和召回率的调和平均值。如果精确度为1，召回为0，那F1为0.还有F0.5和F2两种模型，分别偏重精确率和召回率。在一些场景下，召回率比精确率还更重要。