ML

ML,第1张

机器学习100天系列学习笔记 机器学习100天(中文翻译版)机器学习100天(英文原版)
代码阅读:

第一步:导包
#Step 1: Importing the Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
第二步:导入数据
#Step 2: Importing the dataset
dataset = pd.read_csv('D:/daily/机器学习100天/100-Days-Of-ML-Code-中文版本/100-Days-Of-ML-Code-master/datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
第三步:划分训练集、测试集
#Step 3: Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
第四步:特征缩放
#Step 4: Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

经过特征缩放后的X_train:

[[ 0.58164944 -0.88670699]
 [-0.60673761  1.46173768]
 [-0.01254409 -0.5677824 ]
 [-0.60673761  1.89663484]
 [ 1.37390747 -1.40858358]
 [ 1.47293972  0.99784738]
 [ 0.08648817 -0.79972756]
 [-0.01254409 -0.24885782]
 [-0.21060859 -0.5677824 ]...]

对于进行特征缩放这一步,个人认为是非常重要的,它可以加快收敛速度,在深度学习中间尤为重要(梯度爆炸问题)。


第五步:KNeighborsClassifier
#Step 5: Fitting K-NN to the Training set
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)

KNeighborsClassifier函数的调用官方文档 KNN
这里用的是minkowski (闵可夫斯基距离)
When p = 1, this is equivalent to using manhattan_distance (l_1), and euclidean_distance (l_2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.

第六步:预测
#Step 6: Predicting the Test set results
y_pred = classifier.predict(X_test)
第七步:混淆矩阵
#Step 7: Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
cm = confusion_matrix(y_test, y_pred)
print(cm)  # print confusion_matrix
print(classification_report(y_test, y_pred))   # print classification report

混淆:简单理解为一个class被预测成另一个class。



给一个参考链接 混淆矩阵
然后谈谈classification_report函数;科学上网,正常上网

输出:

[[64  4]
 [ 3 29]]
              precision    recall  f1-score   support

           0       0.96      0.94      0.95        68
           1       0.88      0.91      0.89        32

    accuracy                           0.93       100
   macro avg       0.92      0.92      0.92       100
weighted avg       0.93      0.93      0.93       100

precision:精确度;
recall:召回率;
f1-score:precision、recall的调和函数,越接近1越好;
support:每个标签的出现次数;
avg / total行为各列的均值(support列为总和);

第八步:可视化
#Step 8: Visualization
from matplotlib.colors import ListedColormap
X_set,y_set = X_train,y_train
X1,X2 = np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:,0].max()+1, step=0.01),
                     np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())

for i,j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],
                c = ListedColormap(('red', 'green'))(i), label=j)
plt. title(' KNN(Training set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()

X_set,y_set=X_test,y_test
X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01),
                   np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))

plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())
for i,j in enumerate(np. unique(y_set)):
    plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],
                c = ListedColormap(('red', 'green'))(i), label=j)

plt. title(' KNN(Test set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()

全部代码:

#Day 5: KNN 2022/4/8
#Step 1: Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#Step 2: Importing the dataset
dataset = pd.read_csv('D:/daily/机器学习100/100-Days-Of-ML-Code-中文版本/100-Days-Of-ML-Code-master/datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values

#Step 3: Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

#Step 4: Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

#Step 5: Fitting K-NN to the Training set
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)

#Step 6: Predicting the Test set results
y_pred = classifier.predict(X_test)

#Step 7: Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
cm = confusion_matrix(y_test, y_pred)
print(cm)
print(classification_report(y_test, y_pred))

#Step 8: Visualization
from matplotlib.colors import ListedColormap
X_set,y_set = X_train,y_train
X1,X2 = np. meshgrid(np. arange(start = X_set[:,0].min()-1, stop = X_set[:,0].max()+1, step = 0.01),
                     np. arange(start = X_set[:,1].min()-1, stop = X_set[:,1].max()+1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())
for i,j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],
                c = ListedColormap(('red', 'green'))(i), label=j)
plt. title(' KNN(Training set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()

X_set,y_set = X_test,y_test
X1,X2=np. meshgrid(np. arange(start = X_set[:,0].min()-1, stop = X_set[:,0].max()+1, step = 0.01),
                   np. arange(start = X_set[:,1].min()-1, stop = X_set[:,1].max()+1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())
for i,j in enumerate(np. unique(y_set)):
    plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],
                c = ListedColormap(('red', 'green'))(i), label=j)

plt. title(' KNN(Test set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()

欢迎分享,转载请注明来源:内存溢出

原文地址: http://outofmemory.cn/langs/567640.html

(0)
打赏 微信扫一扫 微信扫一扫 支付宝扫一扫 支付宝扫一扫
上一篇 2022-04-09
下一篇 2022-04-09

发表评论

登录后才能评论

评论列表(0条)

保存