有监督学习教程（Python随机森林示例和实现代码） _Python随机森林代码示例

Python如何实现随机森林？在本指南中，我将向你展示 Python 中的随机森林示例。
一般来说，随机森林是一种有监督的机器学习形式，可用于分类和回归。
在本Python随机森林教程结束时，你将能够创建以下图形用户界面 (GUI) 来执行基于随机森林模型的预测：

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Python随机森林示例假设你的目标是预测候选人是否会被名牌大学录取。有3种可能的结果：

候选人被录取 - 由值2 表示
候选人在等候名单上 - 由值1 表示
候选人不被录取——用0表示

以下是将用于我们的示例的完整数据集：

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在我们的例子中：

在GMAT，GPA，work_experience和年龄是特征变量
在admitted 列表示标签/目标

请注意，上述数据集包含 40 个观察值。实际上，你可能需要更大的样本量才能获得更准确的结果。
在 Python 中应用随机森林的步骤步骤 1：安装相关的 Python 包
如果你还没有这样做，请安装以下 Python 包：

pandas – 用于创建 DataFrame 以在 Python 中捕获数据集
sklearn – 用于执行随机森林
seaborn – 用于创建混淆矩阵
matplotlib – 用于显示图表

你可以应用PIP 安装方法来安装这些软件包。
然后，你需要按如下方式导入 Python 包，如下Python随机森林代码示例：

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn import metrics import seaborn as sn import matplotlib.pyplot as plt

第 2 步：创建数据帧
接下来，创建 DataFrame以捕获我们示例的数据集：

import pandas as pdcandidates = {'gmat': [ 780,750,690,710,780,730,690,720,740,690,610,690,710,680,770,610,580,650,540,590,620,600,550,550,570,670,660,580,650,760,640,620,660,660,680,650,670,580,590,790], 'gpa': [ 4,3.9,3.3,3.7,3.9,3.7,2.3,3.3,3.3,1.7,2.7,3.7,3.7,3.3,3.3,3,2.7,3.7,2.7,2.3,3.3,2,2.3,2.7,3,3.3,3.7,2.3,3.7,3.3,3,2.7,4,3.3,3.3,2.3,2.7,3.3,1.7,3.7], 'work_experience': [ 3,4,3,5,4,6,1,4,5,1,3,5,6,4,3,1,4,6,2,3,2,1,4,1,2,6,4,2,6,5,1,2,4,6,5,1,2,1,4,5], 'age': [ 25,28,24,27,26,31,24,25,28,23,25,27,30,28,26,23,29,31,26,26,25,24,28,23,25,29,28,26,30,30,23,24,27,29,28,22,23,24,28,31], 'admitted': [ 2,2,1,2,2,2,0,2,2,0,0,2,2,1,2,0,0,1,0,0,1,0,0,0,0,1,1,0,1,2,0,0,1,1,1,0,0,0,0,2] }df = pd.DataFrame(candidates,columns= [ 'gmat', 'gpa','work_experience','age','admitted']) print (df)

或者，你可以将数据从外部文件导入 Python。
第 3 步：在 Python 中应用随机森林
Python如何实现随机森林？现在，设置特征（表示为 X）和标签（表示为 y）：

X = df[ [ 'gmat', 'gpa','work_experience','age']] y = df[ 'admitted']

然后，应用 train_test_split。例如，你可以将测试大小设置为 0.25，因此模型测试将基于数据集的 25%，而模型训练将基于数据集的 75%：

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=0)

应用随机森林如下Python随机森林示例：

clf = RandomForestClassifier(n_estimators=100) clf.fit(X_train,y_train) y_pred=clf.predict(X_test)

接下来，添加此代码以获取混淆矩阵：

confusion_matrix = pd.crosstab(y_test, y_pred, rownames=[ 'Actual'], colnames=[ 'Predicted']) sn.heatmap(confusion_matrix, annot=True)

最后，打印精度并绘制混淆矩阵：

print('Accuracy: ',metrics.accuracy_score(y_test, y_pred)) plt.show()

将上述所有组件放在一起：

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn import metrics import seaborn as sn import matplotlib.pyplot as pltcandidates = {'gmat': [ 780,750,690,710,780,730,690,720,740,690,610,690,710,680,770,610,580,650,540,590,620,600,550,550,570,670,660,580,650,760,640,620,660,660,680,650,670,580,590,790], 'gpa': [ 4,3.9,3.3,3.7,3.9,3.7,2.3,3.3,3.3,1.7,2.7,3.7,3.7,3.3,3.3,3,2.7,3.7,2.7,2.3,3.3,2,2.3,2.7,3,3.3,3.7,2.3,3.7,3.3,3,2.7,4,3.3,3.3,2.3,2.7,3.3,1.7,3.7], 'work_experience': [ 3,4,3,5,4,6,1,4,5,1,3,5,6,4,3,1,4,6,2,3,2,1,4,1,2,6,4,2,6,5,1,2,4,6,5,1,2,1,4,5], 'age': [ 25,28,24,27,26,31,24,25,28,23,25,27,30,28,26,23,29,31,26,26,25,24,28,23,25,29,28,26,30,30,23,24,27,29,28,22,23,24,28,31], 'admitted': [ 2,2,1,2,2,2,0,2,2,0,0,2,2,1,2,0,0,1,0,0,1,0,0,0,0,1,1,0,1,2,0,0,1,1,1,0,0,0,0,2] }df = pd.DataFrame(candidates,columns= [ 'gmat', 'gpa','work_experience','age','admitted']) #print (df)X = df[ [ 'gmat', 'gpa','work_experience','age']] y = df[ 'admitted']X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=0)clf = RandomForestClassifier(n_estimators=100) clf.fit(X_train,y_train) y_pred=clf.predict(X_test)confusion_matrix = pd.crosstab(y_test, y_pred, rownames=[ 'Actual'], colnames=[ 'Predicted']) sn.heatmap(confusion_matrix, annot=True)print('Accuracy: ',metrics.accuracy_score(y_test, y_pred)) plt.show()

在 Python 中运行代码，你将获得0.8的准确度，然后是混淆矩阵：

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你还可以从混淆矩阵中得出准确度：
准确度 =（主对角线上值的总和）/（矩阵上所有值的总和）
对于我们的示例：
准确度 = (4+2+2)/(4+2+2+1+1) = 0.8
Python随机森林教程：现在让我们通过在 python 代码中打印以下两个组件来深入研究结果：

print（X_test）
print（y_pred）

这是使用的Python随机森林代码示例：

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn import metricscandidates = {'gmat': [ 780,750,690,710,780,730,690,720,740,690,610,690,710,680,770,610,580,650,540,590,620,600,550,550,570,670,660,580,650,760,640,620,660,660,680,650,670,580,590,790], 'gpa': [ 4,3.9,3.3,3.7,3.9,3.7,2.3,3.3,3.3,1.7,2.7,3.7,3.7,3.3,3.3,3,2.7,3.7,2.7,2.3,3.3,2,2.3,2.7,3,3.3,3.7,2.3,3.7,3.3,3,2.7,4,3.3,3.3,2.3,2.7,3.3,1.7,3.7], 'work_experience': [ 3,4,3,5,4,6,1,4,5,1,3,5,6,4,3,1,4,6,2,3,2,1,4,1,2,6,4,2,6,5,1,2,4,6,5,1,2,1,4,5], 'age': [ 25,28,24,27,26,31,24,25,28,23,25,27,30,28,26,23,29,31,26,26,25,24,28,23,25,29,28,26,30,30,23,24,27,29,28,22,23,24,28,31], 'admitted': [ 2,2,1,2,2,2,0,2,2,0,0,2,2,1,2,0,0,1,0,0,1,0,0,0,0,1,1,0,1,2,0,0,1,1,1,0,0,0,0,2] }df = pd.DataFrame(candidates,columns= [ 'gmat', 'gpa','work_experience','age','admitted']) #print (df)X = df[ [ 'gmat', 'gpa','work_experience','age']] y = df[ 'admitted']X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=0)clf = RandomForestClassifier(n_estimators=100) clf.fit(X_train,y_train) y_pred = clf.predict(X_test)print (X_test) #test dataset (without the actual outcome) print (y_pred) #predicted values

回想一下，我们的原始数据集有 40 个观察值。由于我们将测试大小设置为 0.25，因此混淆矩阵显示了总共 10 条记录的结果（=40*0.25）。这些是 10 个测试记录：

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还对这 10 条记录进行了预测（其中 2 = 被录取，1 = 等待名单，0 = 未被录取）：

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在原始数据集中，你会看到对于测试数据，我们得到了 10 次中的 8 次正确结果：

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这与 80% 的准确度水平一致。
第 4 步：执行预测 - Python随机森林示例
Python如何实现随机森林？现在让我们根据以下信息进行预测，以确定新候选人是否会被录取：

gmat = 730
gpa = 3.7
work_experience = 4
age = 27

【有监督学习教程（Python随机森林示例和实现代码）】然后，你需要添加以下语法来进行预测：

prediction = clf.predict([ [ 730,3.7,4,27]]) print ('Predicted Result: ', prediction)

所以这就是完整代码的Python随机森林代码示例样子：

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifiercandidates = {'gmat': [ 780,750,690,710,780,730,690,720,740,690,610,690,710,680,770,610,580,650,540,590,620,600,550,550,570,670,660,580,650,760,640,620,660,660,680,650,670,580,590,790], 'gpa': [ 4,3.9,3.3,3.7,3.9,3.7,2.3,3.3,3.3,1.7,2.7,3.7,3.7,3.3,3.3,3,2.7,3.7,2.7,2.3,3.3,2,2.3,2.7,3,3.3,3.7,2.3,3.7,3.3,3,2.7,4,3.3,3.3,2.3,2.7,3.3,1.7,3.7], 'work_experience': [ 3,4,3,5,4,6,1,4,5,1,3,5,6,4,3,1,4,6,2,3,2,1,4,1,2,6,4,2,6,5,1,2,4,6,5,1,2,1,4,5], 'age': [ 25,28,24,27,26,31,24,25,28,23,25,27,30,28,26,23,29,31,26,26,25,24,28,23,25,29,28,26,30,30,23,24,27,29,28,22,23,24,28,31], 'admitted': [ 2,2,1,2,2,2,0,2,2,0,0,2,2,1,2,0,0,1,0,0,1,0,0,0,0,1,1,0,1,2,0,0,1,1,1,0,0,0,0,2] }df = pd.DataFrame(candidates,columns= [ 'gmat', 'gpa','work_experience','age','admitted']) #print (df)X = df[ [ 'gmat', 'gpa','work_experience','age']] y = df[ 'admitted']X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=0)clf = RandomForestClassifier(n_estimators=100) clf.fit(X_train,y_train) y_pred = clf.predict(X_test)prediction = clf.predict([ [ 730,3.7,4,27]]) print ('Predicted Result: ', prediction)

运行代码后，你将获得2的值，这意味着该候选人有望被录取：

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你可以通过创建一个简单的图形用户界面 (GUI)来更进一步，你可以在其中输入特征变量以获得预测。
以下是可用于创建 GUI 的完整Python随机森林代码示例（基于 tkinter 包）：

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier import tkinter as tk candidates = {'gmat': [ 780,750,690,710,780,730,690,720,740,690,610,690,710,680,770,610,580,650,540,590,620,600,550,550,570,670,660,580,650,760,640,620,660,660,680,650,670,580,590,790], 'gpa': [ 4,3.9,3.3,3.7,3.9,3.7,2.3,3.3,3.3,1.7,2.7,3.7,3.7,3.3,3.3,3,2.7,3.7,2.7,2.3,3.3,2,2.3,2.7,3,3.3,3.7,2.3,3.7,3.3,3,2.7,4,3.3,3.3,2.3,2.7,3.3,1.7,3.7], 'work_experience': [ 3,4,3,5,4,6,1,4,5,1,3,5,6,4,3,1,4,6,2,3,2,1,4,1,2,6,4,2,6,5,1,2,4,6,5,1,2,1,4,5], 'age': [ 25,28,24,27,26,31,24,25,28,23,25,27,30,28,26,23,29,31,26,26,25,24,28,23,25,29,28,26,30,30,23,24,27,29,28,22,23,24,28,31], 'admitted': [ 2,2,1,2,2,2,0,2,2,0,0,2,2,1,2,0,0,1,0,0,1,0,0,0,0,1,1,0,1,2,0,0,1,1,1,0,0,0,0,2] }df = pd.DataFrame(candidates,columns= [ 'gmat', 'gpa','work_experience','age','admitted']) #print (df)X = df[ [ 'gmat', 'gpa','work_experience','age']] y = df[ 'admitted']X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=0)clf = RandomForestClassifier(n_estimators=100) clf.fit(X_train,y_train) y_pred = clf.predict(X_test)# tkinter GUI root= tk.Tk()canvas1 = tk.Canvas(root, width = 500, height = 350) canvas1.pack()# GMAT label1 = tk.Label(root, text='GMAT:') canvas1.create_window(100, 100, window=label1)entry1 = tk.Entry (root) canvas1.create_window(270, 100, window=entry1)# GPA label2 = tk.Label(root, text='GPA:') canvas1.create_window(120, 120, window=label2)entry2 = tk.Entry (root) canvas1.create_window(270, 120, window=entry2)# work_experience label3 = tk.Label(root, text='Work Experience: ') canvas1.create_window(140, 140, window=label3)entry3 = tk.Entry (root) canvas1.create_window(270, 140, window=entry3)# Age input label4 = tk.Label(root, text='Age:') canvas1.create_window(160, 160, window=label4)entry4 = tk.Entry (root) canvas1.create_window(270, 160, window=entry4)def values(): global gmat gmat = float(entry1.get()) global gpa gpa = float(entry2.get()) global work_experience work_experience = float(entry3.get()) global age age = float(entry4.get()) Prediction_result= ('Predicted Result: ', clf.predict([ [ gmat,gpa,work_experience,age]])) label_Prediction = tk.Label(root, text= Prediction_result, bg='sky blue') canvas1.create_window(270, 280, window=label_Prediction)button1 = tk.Button (root, text='Predict',command=values, bg='green', fg='white', font=11) canvas1.create_window(270, 220, window=button1) root.mainloop()

运行代码，你会得到这样的显示：

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为新候选人键入以下值：

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在输入框中输入完值后，单击“预测”按钮，你将得到 2 的预测（即，候选人有望被录取）：

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你可以尝试不同的值组合以查看预测结果。
Python随机森林教程：如何确定特征的重要性Python如何实现随机森林？在本指南的最后一节中，你将看到如何获得特征的重要性分数。一般来说，你可以考虑排除得分较低的特征。
以下是你需要添加以获得功能重要性的语法，如下Python随机森林代码示例：

featureImportances = pd.Series(clf.feature_importances_).sort_values(ascending=False) print(featureImportances)sn.barplot(x=round(featureImportances,4), y=featureImportances) plt.xlabel('Features Importance') plt.show()

这是完整的Python随机森林示例代码（确保还导入了 matplotlib 包）：

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier import seaborn as sn import matplotlib.pyplot as pltcandidates = {'gmat': [ 780,750,690,710,780,730,690,720,740,690,610,690,710,680,770,610,580,650,540,590,620,600,550,550,570,670,660,580,650,760,640,620,660,660,680,650,670,580,590,790], 'gpa': [ 4,3.9,3.3,3.7,3.9,3.7,2.3,3.3,3.3,1.7,2.7,3.7,3.7,3.3,3.3,3,2.7,3.7,2.7,2.3,3.3,2,2.3,2.7,3,3.3,3.7,2.3,3.7,3.3,3,2.7,4,3.3,3.3,2.3,2.7,3.3,1.7,3.7], 'work_experience': [ 3,4,3,5,4,6,1,4,5,1,3,5,6,4,3,1,4,6,2,3,2,1,4,1,2,6,4,2,6,5,1,2,4,6,5,1,2,1,4,5], 'age': [ 25,28,24,27,26,31,24,25,28,23,25,27,30,28,26,23,29,31,26,26,25,24,28,23,25,29,28,26,30,30,23,24,27,29,28,22,23,24,28,31], 'admitted': [ 2,2,1,2,2,2,0,2,2,0,0,2,2,1,2,0,0,1,0,0,1,0,0,0,0,1,1,0,1,2,0,0,1,1,1,0,0,0,0,2] }df = pd.DataFrame(candidates,columns= [ 'gmat', 'gpa','work_experience','age','admitted'])X = df[ [ 'gmat', 'gpa','work_experience','age']] y = df[ 'admitted']X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=0)clf = RandomForestClassifier(n_estimators=100) clf.fit(X_train,y_train) y_pred = clf.predict(X_test)featureImportances = pd.Series(clf.feature_importances_).sort_values(ascending=False) print(featureImportances)sn.barplot(x=round(featureImportances,4), y=featureImportances) plt.xlabel('Features Importance') plt.show()

正如你所观察到的，年龄的分数较低（即 0.046941），因此可能会被排除在模型之外：