机器学习分类算法实例，分别写出简单的10个例子python

好的，下面是10个机器学习分类算法的Python实例：

1. 决策树分类

from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris

iris = load_iris()
X, y = iris.data, iris.target

clf = DecisionTreeClassifier()
clf.fit(X, y)

2. 支持向量机分类

from sklearn.svm import SVC
from sklearn.datasets import load_iris

iris = load_iris()
X, y = iris.data, iris.target

clf = SVC()
clf.fit(X, y)

3. K近邻分类

from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris

iris = load_iris()
X, y = iris.data, iris.target

clf = KNeighborsClassifier()
clf.fit(X, y)

4. 朴素贝叶斯分类

from sklearn.naive_bayes import GaussianNB
from sklearn.datasets import load_iris

iris = load_iris()
X, y = iris.data, iris.target

clf = GaussianNB()
clf.fit(X, y)

5. 随机森林分类

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris

iris = load_iris()
X, y = iris.data, iris.target

clf = RandomForestClassifier()
clf.fit(X, y)

6. AdaBoost分类

from sklearn.ensemble import AdaBoostClassifier
from sklearn.datasets import load_iris

iris = load_iris()
X, y = iris.data, iris.target

clf = AdaBoostClassifier()
clf.fit(X, y)

7. 梯度提升分类

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import load_iris

iris = load_iris()
X, y = iris.data, iris.target

clf = GradientBoostingClassifier()
clf.fit(X, y)

8. 神经网络分类

from sklearn.neural_network import MLPClassifier
from sklearn.datasets import load_iris

iris = load_iris()
X, y = iris.data, iris.target

clf = MLPClassifier()
clf.fit(X, y)

9. LDA分类

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.datasets import load_iris

iris = load_iris()
X, y = iris.data, iris.target

clf = LinearDiscriminantAnalysis()
clf.fit(X, y)

10. QDA分类

from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.datasets import load_iris

iris = load_iris()
X, y = iris.data, iris.target

clf = QuadraticDiscriminantAnalysis()
clf.fit(X, y)