python sklearn

#回归
x = [[3,6],[6,9],[9,12],[12,15],[15,18],[18,21],[21,23]]
y = [[3],[6],[9],[12],[15],[18],[21]]

x1 = [[2,4],[4,6],[6,8],[8,10],[10,12]]
y1 = [[2],[4],[6],[8],[10]]
# #线性回归
# from sklearn.linear_model import LinearRegression
# model = LinearRegression ()
# model.fit(x,y)
# print(model.intercept_) #截距
# print(model.coef_) #线性模型的系数
# print(model.score(x1,y1)) #模型评分，预测率
# print(model.predict(x1)) #预测
#
# #lasso回归
# from sklearn.linear_model import Lasso
# lasso = Lasso(alpha=0.1)
# lasso.fit(x,y)
# print(lasso.score(x1,y1))
# print(lasso.predict(x))
# print(lasso.intercept_) #截距
# print(lasso.coef_) #线性模型系数
# print(lasso.n_iter_ ) #迭代数
#
# #ridge回归
# from sklearn.linear_model import Ridge
# ridge = Ridge(alpha=1.0)
# ridge.fit(x,y)
# print(ridge.score(x1,y1))
# print(ridge.predict(x1))

#分类
from sklearn import datasets
from sklearn.model_selection import train_test_split
iris = datasets.load_iris()
# print(iris)
iris_x = iris.data
iris_y = iris.target
# print(iris_x)
x_train,x_test,y_train,y_test = train_test_split(iris_x,iris_y,test_size=0.3) #切割数据集

# #对数几率回归/逻辑回归
# from sklearn.linear_model import LogisticRegression
# clf = LogisticRegression(max_iter= 1000)
# clf.fit(x_train,y_train)
# print(clf.score(x_test,y_test))
# print(clf.predict(x_test))


# # lda 线性判别分析
# from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
# lda = LinearDiscriminantAnalysis ()
# lda.fit(x_train,y_train)
# print(lda.score(x_test,y_test)) #预测值
# print(lda.predict(x_test)) #预测

# #k近邻
# from sklearn.neighbors import KNeighborsClassifier
# knn = KNeighborsClassifier () #
# knn.fit(x_train,y_train) #拟合
# print(knn.score(x_test,y_test)) #预测率
# print(knn.predict(x_test)) #预测

#支持向量机
# #线性可分支持向量机
# from sklearn.svm import LinearSVC
# svc = LinearSVC(max_iter=10000) #penalty='l2', loss='squared_hinge', *, dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000
# svc.fit(x_train,y_train)
# print(svc.score(x_test,y_test))
# print(svc.predict(x_test))
#
# #近拟可分支持向量机
# from sklearn.svm import SVC
# svc = SVC(kernel='linear')
# svc.fit(x_train,y_train)
# print(svc.score(x_test,y_test))
# print(svc.predict(x_test))
#
# #不可分支持向量机
# from sklearn.svm import SVC
# svc  = SVC(kernel='rbf')
# svc.fit(x_train,y_train)
# print(svc.score(x_test,y_test))
# print(svc.predict(x_test))


#综合
#决策树
# # cart 分类树
# from sklearn.tree import DecisionTreeClassifier
# tree = DecisionTreeClassifier()
# tree.fit(x_train,y_train)
# print(tree.score(x_test,y_test))
# print(tree.predict(x_test))
# # cart 回归树
# from sklearn.tree import DecisionTreeRegressor
# tree = DecisionTreeRegressor()
# tree.fit(x,y)
# print(tree.score(x1,y1))
# print(tree.predict(x1))

#神经网络