【发布时间】：2021-06-25 01:15:30
【问题描述】：
我想使用准确率、精确度、召回率和 F-measure 作为绩效指标。在准确度的情况下，代码可以正常工作，但是当有很多指标时，我会出错。我想知道我怎么能做到这一点？。
import matplotlib.pyplot as plt
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, f1_score
scoring = {'accuracy' : make_scorer(accuracy_score),
'precision' : make_scorer(precision_score),
'recall' : make_scorer(recall_score),
'f1_score' : make_scorer(f1_score)}
# load dataset
# prepare configuration for cross validation test harness
seed = 7
# prepare models
models = []
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
#models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC()))
# evaluate each model in turn
results = []
names = []
#scoring = 'accuracy'
for name, model in models:
kfold = model_selection.KFold(n_splits=5, random_state=seed)
cv_results = model_selection.cross_validate(model, X_, y, cv=kfold, scoring=scoring)
results.append(cv_results)
names.append(name)
'''
msg = "%s: %f (%f)" % (name, cv_results['accuracy'].mean(), cv_results['accuracy'].std())
msg2 = "%s: %f (%f)" % (name, cv_results['precision'].mean(), cv_results['precision'].std())
msg3 = "%s: %f (%f)" % (name, cv_results['recall'].mean(), cv_results['recall'].std())
msg4 = "%s: %f (%f)" % (name, cv_results['f1_score'].mean(), cv_results['f1_score'].std())
print(msg)
print(msg2)
print(msg3)
print(msg4)
'''
以下代码用于显示模型的准确度结果，以防我们将准确度作为唯一评分。我想对其进行编辑并使其适用于上述我有许多评分功能的情况。我该怎么做？
# boxplot algorithm comparison
fig = plt.figure()
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()
results 有这些值，我想知道如何获得指标分数：
[{'fit_time': array([0.05684781, 0.03089881, 0.04285073, 0.03789902, 0.04088998]),
'score_time': array([0.00798011, 0.00497937, 0.00498676, 0.00598478, 0.00398898]),
'test_accuracy': array([0.95977011, 0.94827586, 0.96551724, 0.95677233, 0.94524496]),
'test_precision': array([0.95209581, 0.94886364, 0.97633136, 0.97701149, 0.93785311]),
'test_recall': array([0.96363636, 0.94886364, 0.95375723, 0.93922652, 0.95402299]),
'test_f1': array([0.95783133, 0.94886364, 0.96491228, 0.95774648, 0.94586895])},
{'fit_time': array([0.01396322, 0.00897574, 0.01296639, 0.0089767 , 0.01097035]),
'score_time': array([0.0069809 , 0.0079782 , 0.00698042, 0.0069809 , 0.00598478]),
'test_accuracy': array([0.97701149, 0.95402299, 0.96264368, 0.95389049, 0.97982709]),
'test_precision': array([0.99371069, 0.97058824, 0.99382716, 1. , 0.99408284]),
'test_recall': array([0.95757576, 0.9375 , 0.93063584, 0.91160221, 0.96551724]),
'test_f1': array([0.97530864, 0.95375723, 0.96119403, 0.95375723, 0.97959184])},
{'fit_time': array([0.00698161, 0.00698113, 0.00698113, 0.0039897 , 0.00498629]),
'score_time': array([0.00598383, 0.00598478, 0.00398827, 0.0039897 , 0.00498652]),
'test_accuracy': array([1. , 1. , 1. , 0.99711816, 1. ]),
'test_precision': array([1., 1., 1., 1., 1.]),
'test_recall': array([1. , 1. , 1. , 0.99447514, 1. ]),
'test_f1': array([1. , 1. , 1. , 0.99722992, 1. ])},
{'fit_time': array([0.00398946, 0.00399137, 0.00498724, 0.00299191, 0.00299263]),
'score_time': array([0.00398922, 0.00498629, 0.00697994, 0.00498533, 0.00698185]),
'test_accuracy': array([0.87068966, 0.89655172, 0.90229885, 0.88760807, 0.88184438]),
'test_precision': array([0.78571429, 0.83018868, 0.83574879, 0.82272727, 0.80930233]),
'test_recall': array([1., 1., 1., 1., 1.]),
'test_f1': array([0.88 , 0.90721649, 0.91052632, 0.90274314, 0.89460154])},
{'fit_time': array([0.03992987, 0.04884362, 0.04388309, 0.03992462, 0.03992629]),
'score_time': array([0.01694345, 0.01100636, 0.01097107, 0.0119369 , 0.01093674]),
'test_accuracy': array([0.9683908 , 0.95689655, 0.97413793, 0.95389049, 0.97982709]),
'test_precision': array([0.99358974, 1. , 0.9939759 , 1. , 1. ]),
'test_recall': array([0.93939394, 0.91477273, 0.95375723, 0.91160221, 0.95977011]),
'test_f1': array([0.96573209, 0.95548961, 0.97345133, 0.95375723, 0.97947214])}]
【问题讨论】：
-
结果列表具有这些值。我在寻找。如何从列表中获取准确率、召回率等？
标签： machine-learning scikit-learn cross-validation