由于端午...所以這次作業(yè)現(xiàn)在才提交...
作業(yè)步驟
直接上代碼
from sklearn import datasets, metrics
from sklearn.model_selection import KFold
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
import numpy as np
def result_output(y_test, pred):
print("Accuracy: ", metrics.accuracy_score(y_test, pred))
try:
print("F1-score: ", metrics.f1_score(y_test, pred))
except:
print("F1-score Error!")
try:
print("AUC ROC ", metrics.roc_auc_score(y_test, pred))
except ValueError:
print("ROC AUC score is not defined when only one class present in y_true")
# dataset = datasets.load_wine(return_X_y=True)
dataset = datasets.make_classification(n_samples=2000, n_features=10)
kf = KFold(n_splits=10)
k = 0
for train_index, test_index in kf.split(dataset[0]):
X_train, X_test = dataset[0][train_index], dataset[0][test_index]
y_train, y_test = dataset[1][train_index], dataset[1][test_index]
k += 1
print("***********************************")
print("Test %d" % k)
print("Naive Bayes:")
clf = GaussianNB()
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
result_output(y_test, pred)
print("---------------------------")
print("SVM:")
clf = SVC(C=1e-2, kernel='rbf', gamma=0.1)
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
result_output(y_test, pred)
print("---------------------------")
print("Random Forest:")
clf = RandomForestClassifier(n_estimators=10)
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
result_output(y_test, pred)
以下是其中的若干次結(jié)果:
Test 1
Test 5
Test 9
