机器学习多分类,情感分析

将多分类转换为二分类来进行相应的计算

相关代码：

1. LogisticRegression

	from sklearn import datasets
	from sklearn.model_selection import train_test_split
	from sklearn.multiclass import OneVsRestClassifier
	from sklearn.multiclass import OneVsOneClassifier
	from sklearn.linear_model import LogisticRegression
	from sklearn.metrics import accuracy_score
	data_iris = datasets.load_iris()
	x, y = data_iris.data, data_iris.target
	x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3,random_state = 0)
	# 使用multiclass的OvO多分类策略,分类器使用LogisticRegression
	model = OneVsOneClassifier(LogisticRegression(C=1.0, tol=1e-6))
	model.fit(x_train, y_train)
	y_pred = model.predict(x_test)
	print(accuracy_score(y_test, y_pred))
	============================================
	0.9555555555555556
	# 使用multiclass的OvR多分类策略,分类器使用LogisticRegression
	model = OneVsRestClassifier(LogisticRegression(C=1.0, tol=1e-6))
	model.fit(x_train, y_train)
	y_pred = model.predict(x_test)
	print(accuracy_score(y_test, y_pred))
	============================================
	0.8888888888888888

2. SVM

SVC

	from sklearn import datasets
	from sklearn.model_selection import train_test_split
	from sklearn.multiclass import OneVsRestClassifier
	from sklearn.multiclass import OneVsOneClassifier
	from sklearn import svm
	from sklearn.metrics import accuracy_score
	data_iris = datasets.load_iris()
	x, y = data_iris.data, data_iris.target
	x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3,random_state = 0)
	# 使用multiclass的OvO多分类策略,分类器使用SVM
	model = OneVsOneClassifier(svm.SVC())
	model.fit(x_train, y_train)
	y_pred = model.predict(x_test)
	print(accuracy_score(y_test, y_pred))
	============================================
	0.9555555555555556
	# 使用multiclass的OvR多分类策略,分类器使用SVM
	model = OneVsRestClassifier(svm.SVC())
	model.fit(x_train, y_train)
	y_pred = model.predict(x_test)
	print(accuracy_score(y_test, y_pred))
	============================================
	0.8888888888888888

LinearSVC

	# 使用multiclass的OvR多分类策略,分类器使用SVM
	model = OneVsRestClassifier(svm.LinearSVC())
	model.fit(x_train, y_train)
	y_pred = model.predict(x_test)
	print(accuracy_score(y_test, y_pred))

3. Decision Tree

	from sklearn.tree import DecisionTreeClassifier
	model = DecisionTreeClassifier()
	model.fit(x_train, y_train)
	y_pred = model.predict(x_test)
	print(accuracy_score(y_test, y_pred))

4. Random Forest

	from sklearn.ensemble import RandomForestClassifier
	model = RandomForestClassifier(n_jobs=2)
	model.fit(x_train, y_train)
	y_pred = model.predict(x_test)
	print(accuracy_score(y_test, y_pred))

5. AdaBoost

	from sklearn.ensemble import AdaBoostClassifier
	model = AdaBoostClassifier(DecisionTreeClassifier(),
	algorithm="SAMME",
	n_estimators=200,
	learning_rate=0.5)
	model.fit(x_train, y_train)
	y_pred = model.predict(x_test)
	print(accuracy_score(y_test, y_pred))

6. 朴素贝叶斯

MultinomialNB

	from sklearn.naive_bayes import MultinomialNB, GaussianNB
	model = MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)
	model.fit(x_train, y_train)
	y_pred = model.predict(x_test)
	print(accuracy_score(y_test, y_pred))

GaussianNB

	model = GaussianNB()
	model.fit(x_train, y_train)
	y_pred = model.predict(x_test)
	print(accuracy_score(y_test, y_pred))

BernoulliNB

	model = BernoulliNB()
	model.fit(x_train, y_train)
	y_pred = model.predict(x_test)
	print(accuracy_score(y_test, y_pred))

7. KNN

	from sklearn.neighbors import KNeighborsClassifier
	model = KNeighborsClassifier()
	model.fit(x_train, y_train)
	y_pred = model.predict(x_test)
	print(accuracy_score(y_test, y_pred))

8. GradientBoosting

	from sklearn.ensemble import GradientBoostingClassifier
	model = GradientBoostingClassifier()
	model.fit(x_train, y_train)
	y_pred = model.predict(x_test)
	print(accuracy_score(y_test, y_pred))

PYTHON 复制 全屏

9. XGBoost

	from xgboost.sklearn import XGBClassifier
	model = XGBClassifier()
	model.fit(x_train, y_train)
	y_pred = model.predict(x_test)
	print(accuracy_score(y_test, y_pred))