使用交叉验证的多性能指标

如何解决使用交叉验证的多性能指标

我想使用准确率、准确率、召回率和 F-measure 作为性能指标。在只是准确性的情况下，代码工作正常，但是当有很多指标时，我会出错。我想知道我该怎么做？。

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)
    '''

下面的代码用于显示模型的准确性结果，以防我们将准确性作为唯一的评分。我想编辑它并使其适用于我有许多评分功能的上述情况。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([0.96363636,0.95375723,0.93922652,0.95402299]),'test_f1': array([0.95783133,0.96491228,0.95774648,0.94586895])},{'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([0.97530864,0.96119403,0.97959184])},{'fit_time': array([0.00698161,0.00698113,0.0039897,0.00498629]),'score_time': array([0.00598383,0.00398827,0.00498652]),'test_accuracy': array([1.,0.99711816,1.        ]),'test_precision': array([1.,1.]),'test_recall': array([1.,0.99447514,'test_f1': array([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_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,'test_precision': array([0.99358974,0.9939759,'test_recall': array([0.93939394,0.91477273,0.95977011]),'test_f1': array([0.96573209,0.95548961,0.97345133,0.97947214])}]

解决方法

如documentation中所述：

对于单个指标评估，其中评分参数是字符串、可调用或无，键将是 - ['test_score','fit_time','score_time']

对于多指标评估，返回值是一个带有以下键的字典 - ['test_','test_','test_','fit_time ','score_time']

在你的情况下，你可以获得准确率/召回率/等

cv_results["test_accuracy"]
cv_results["test_recall"]
...