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机器学习常用算法介绍及演示

1. kNN

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
%matplotlib inline

# 加载数据集
fruits_df = pd.read_table('fruit_data_with_colors.txt')

X = fruits_df[['mass', 'width', 'height', 'color_score']]
y = fruits_df['fruit_label']

# 分割数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/4, random_state=0)

# 建立模型
knn = KNeighborsClassifier(n_neighbors=5)

# 训练模型
knn.fit(X_train, y_train)

# 验证模型
y_pred = knn.predict(X_test)

acc = accuracy_score(y_test, y_pred)
print('准确率：', acc)

准确率： 0.533333333333

2. 线性回归

# 人工生成用于测试回归的数据集
import matplotlib.pyplot as plt
from sklearn.datasets import make_regression
%matplotlib inline

plt.figure()
plt.title('Sample regression problem with one input variable')

# 每个样本只有一个变量
X_R1, y_R1 = make_regression(n_samples = 100, n_features=1,
                            n_informative=1, bias = 150.0,
                            noise = 30, random_state=0)
plt.scatter(X_R1, y_R1, marker= 'o', s=50)
plt.show()

png

from sklearn.linear_model import LinearRegression

X_train, X_test, y_train, y_test = train_test_split(X_R1, y_R1,
                                                   random_state = 0)
# 调用线型回归模型
linreg = LinearRegression()

# 训练模型
linreg.fit(X_train, y_train)

# 输出结果
print('线型模型的系数(w): {}'.format(linreg.coef_))
print('线型模型的常数项(b): {:.3f}'.format(linreg.intercept_))
print('训练集中R-squared得分: {:.3f}'.format(linreg.score(X_train, y_train)))
print('测试集中R-squared得分: {:.3f}'.format(linreg.score(X_test, y_test)))

线型模型的系数(w): [ 45.70870465]
线型模型的常数项(b): 148.446
训练集中R-squared得分: 0.679
测试集中R-squared得分: 0.492

# 可视化输出结果

plt.figure(figsize=(5,4))
plt.scatter(X_R1, y_R1, marker= 'o', s=50, alpha=0.8)
plt.plot(X_R1, linreg.coef_ * X_R1 + linreg.intercept_, 'r-')
plt.title('Least-squares linear regression')
plt.xlabel('Feature value (x)')
plt.ylabel('Target value (y)')
plt.show()

png

3. 逻辑回归

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from ml_visualization import plot_class_regions_for_classifier

# 加载数据集
fruits_df = pd.read_table('fruit_data_with_colors.txt')

X = fruits_df[['width', 'height']]
y = fruits_df['fruit_label'].copy()

# 将不是apple的标签设为0
y[y != 1] = 0
# 分割数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/4, random_state=0)

# 不同的C值
c_values = [0.1, 1, 100]

for c_value in c_values:
    # 建立模型
    lr_model = LogisticRegression(C=c_value)

    # 训练模型
    lr_model.fit(X_train, y_train)

    # 验证模型
    y_pred = lr_model.predict(X_test)

    acc = accuracy_score(y_test, y_pred)
    print('C={}，准确率：{:.3f}'.format(c_value, acc))
    
    # 可视化
    plot_class_regions_for_classifier(lr_model, X_test.values, y_test.values, title='C={}'.format(c_value))

C=0.1，准确率：0.667

png

C=1，准确率：0.600

png

C=100，准确率：0.733

png

4. SVM

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC

# 加载数据集
fruits_df = pd.read_table('fruit_data_with_colors.txt')

X = fruits_df[['width', 'height']]
y = fruits_df['fruit_label'].copy()

# 将不是apple的标签设为0
y[y != 1] = 0
# 分割数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/4, random_state=0)

# 不同的C值
c_values = [0.0001, 1, 10000]

for c_value in c_values:
    # 建立模型
    svm_model = SVC(C=c_value)

    # 训练模型
    svm_model.fit(X_train, y_train)

    # 验证模型
    y_pred = svm_model.predict(X_test)

    acc = accuracy_score(y_test, y_pred)
    print('C={}，准确率：{:.3f}'.format(c_value, acc))
    
     # 可视化
    plot_class_regions_for_classifier(lr_model, X_test.values, y_test.values, title='C={}'.format(c_value))

C=0.0001，准确率：0.733

png

C=1，准确率：0.600

png

C=10000，准确率：0.533

png

5. 决策树

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

iris = load_iris()

X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=0)

max_depth_values = [2, 3, 4]

for max_depth_val in max_depth_values:
    dt_model = DecisionTreeClassifier(max_depth=max_depth_val)
    dt_model.fit(X_train, y_train)
    
    print('max_depth=', max_depth_val)
    print('训练集上的准确率: {:.3f}'.format(dt_model.score(X_train, y_train)))
    print('测试集的准确率: {:.3f}'.format(dt_model.score(X_test, y_test)))
    print()

max_depth= 2
训练集上的准确率: 0.964
测试集的准确率: 0.895

max_depth= 3
训练集上的准确率: 0.982
测试集的准确率: 0.974

max_depth= 4
训练集上的准确率: 1.000
测试集的准确率: 0.974

决策树结果可视化：

需要安装:

from ml_visualization import plot_decision_tree

dt_model = DecisionTreeClassifier(max_depth=4)
dt_model.fit(X_train, y_train)
plot_decision_tree(dt_model, iris.feature_names, iris.target_names)

svg

print(iris.feature_names)
print(dt_model.feature_importances_)

['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
[ 0.02014872  0.02014872  0.40530263  0.55439994]

from ml_visualization import plot_feature_importances
plot_feature_importances(dt_model, iris.feature_names)

png
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