[分类算法]常用功能实现

前言：分类是机器学习中的重要的一种功能，在机器学习的研究历史中，诞生了大量的分类算法，而每种算法都有其优势和不足。

本文汇总了常用的分类算法及其实现方式，方便快速查询使用。（本文使用鸢尾花数据集，是三类别分类）

以下9种分类算法，使用相同的数据进行训练和测试，在测试集上的准确率(accuracy)分别为：

1.随机森林：100%

2.决策树：100%

3.K近邻：100%

4.支持向量机：100%

5.逻辑回归：96.67%

6.线性支持向量机：100%

7.随机梯度下降：96.67%

8.感知机：100%

9.朴素贝叶斯：96.67%

1 import numpy as np

2 import pandas as pd

3 import matplotlib as mpl

4 import matplotlib.pyplot as plt

5 import sklearn

6 from sklearn import datasets

7 from sklearn.metrics import accuracy_score

8

9 from sklearn.ensemble import RandomForestClassifier

10 from sklearn.tree import DecisionTreeClassifier

11

12 from sklearn.neighbors import KNeighborsClassifier

13 from sklearn.svm import SVC, LinearSVC

14 from sklearn.linear_model import LogisticRegression

15

16 from sklearn.linear_model import SGDClassifier

17 from sklearn.linear_model import Perceptron

18 from sklearn.naive_bayes import GaussianNB

19

20 from sklearn.model_selection import train_test_split

21 from sklearn.model_selection import cross_val_score

22

23 from sklearn.model_selection import GridSearchCV

24

25 iris = datasets.load_iris()

26 x,y = iris.data,iris.target

27

28 x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=0)

29

30 res = []

31

32 #1. 随机森林分类

33 print('随机森林分类')

34 clf = RandomForestClassifier(n_estimators=100)

35 clf.fit(x_train, y_train)

36 cross_score = cross_val_score(clf, x_train, y_train, cv=3, scoring="accuracy")

37 print(cross_score)

38 y_predict = clf.predict(x_test)

39 score = accuracy_score(y_test,y_predict)

40 res.append(score)

41 print()

42

43 #2. 决策树分类

44 print('决策树分类')

45 clf = DecisionTreeClassifier()

46 clf.fit(x_train, y_train)

47 cross_score = cross_val_score(clf, x_train, y_train, cv=3, scoring="accuracy")

48 print(cross_score)

49 y_predict = clf.predict(x_test)

50 score = accuracy_score(y_test,y_predict)

51 res.append(score)

52 print()

53

54 #3. KNN

55 print('KNN')

56 clf = KNeighborsClassifier(n_neighbors = 13)

57 clf.fit(x_train, y_train)

58 cross_score = cross_val_score(clf, x_train, y_train, cv=3, scoring="accuracy")

59 print(cross_score)

60 y_predict = clf.predict(x_test)

61 score = accuracy_score(y_test,y_predict)

62 res.append(score)

63 print()

64

65 #4. SVM分类

66 print('SVM')

67 clf = SVC(gamma='scale')

68 clf.fit(x_train, y_train)

69 cross_score = cross_val_score(clf, x_train, y_train, cv=3, scoring="accuracy")

70 print(cross_score)

71 y_predict = clf.predict(x_test)

72 score = accuracy_score(y_test,y_predict)

73 res.append(score)

74 print()

75

76 #5. 逻辑回归分类

77 print('LogisticRegression')

78 clf = LogisticRegression(solver='lbfgs',multi_class='ovr')

79 clf.fit(x_train, y_train)

80 cross_score = cross_val_score(clf, x_train, y_train, cv=3, scoring="accuracy")

81 print(cross_score)

82 y_predict = clf.predict(x_test)

83 score = accuracy_score(y_test,y_predict)

84 res.append(score)

85 print()

86

87 #6. linear svm分类

88 print('linear SVM')

89 clf = LinearSVC(max_iter=10000)

90 clf.fit(x_train, y_train)

91 cross_score = cross_val_score(clf, x_train, y_train, cv=3, scoring="accuracy")

92 print(cross_score)

93 y_predict = clf.predict(x_test)

94 score = accuracy_score(y_test,y_predict)

95 res.append(score)

96 print()

97

98 #7. 随机梯度下降分类

99 print('SGD')

100 clf = SGDClassifier(max_iter=1000,tol=1e-3)

101 clf.fit(x_train, y_train)

102 cross_score = cross_val_score(clf, x_train, y_train, cv=3, scoring="accuracy")

103 print(cross_score)

104 y_predict = clf.predict(x_test)

105 score = accuracy_score(y_test,y_predict)

106 res.append(score)

107 print()

108

109 #8. 感知机分类

110 print('Perceptron')

111 clf = Perceptron(max_iter=1000,tol=1e-3)

112 clf.fit(x_train, y_train)

113 cross_score = cross_val_score(clf, x_train, y_train, cv=3, scoring="accuracy")

114 print(cross_score)

115 y_predict = clf.predict(x_test)

116 score = accuracy_score(y_test,y_predict)

117 res.append(score)

118 print()

119

120 #9. 朴素贝叶斯分类

121 print('Naive Bayes')

122 clf = GaussianNB()

123 clf.fit(x_train, y_train)

124 cross_score = cross_val_score(clf, x_train, y_train, cv=3, scoring="accuracy")

125 print(cross_score)

126 y_predict = clf.predict(x_test)

127 score = accuracy_score(y_test,y_predict)

128 res.append(score)

129 print()

130

131 #10. 得分比较

132 print(res)