SoutiPlot

#valores = final_dataset.values
X = final_dataset.iloc[:,:-1]
Y = final_dataset.iloc[:,-1]
#Creamos un array con los modelos que emplearemos
models = []
models.append(('SVM', SVC(kernel='rbf')))
models.append(('Naive_Bayes', GaussianNB()))
models.append(('Decision_Trees', DecisionTreeClassifier()))

results = []
names = []
for name, model in models:
    KF = KFold(n_splits=5)
    cv_results = cross_val_score(model, X, Y, cv=KF.get_n_splits(), scoring='accuracy')
    results.append(cv_results)
    names.append(name)
    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
    print(msg)
# boxplot algorithm comparison
fig = plt.figure()
fig.suptitle("Algorithm accuracy comparison")
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()