multi performance metrics using cross validation

I want to use accuracy, precision, recall, and F-measure as performance metrics. In the case of just accuracy, the code works fine, but when there are many metrics, I get errors. I wonder how I can do that?.

#import pandas  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)      '''  

The below code is used to show the accuracy results of the models in case we have accuracy as only scoring. I want to edit it and make it work for the above case where I have many scoring functions. How I can do that?

# 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 has the those values, I wonder what to do to get the metrics scores :

[{'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])}]

https://stackoverflow.com/questions/66846926/multi-performance-metrics-using-cross-validation March 29, 2021 at 06:48AM