机器学习-随机森林(Random Forest)

2019-10-16 16:29:07 浏览数 (1)

背景介绍

随机森林是一组决策树的商标术语。在随机森林中，我们收集了决策树（也称为“森林”）。为了基于属性对新对象进行分类，每棵树都有一个分类，我们称该树对该类“投票”。森林选择投票最多的类别（在森林中的所有树木上）。

每棵树的种植和生长如下：

  1. 如果训练集中的案例数为N，则随机抽取N个案例样本，但要进行替换。 该样本将成为树木生长的训练集。
  2. 如果有M个输入变量，则指定数字m << M，以便在每个节点上从M个中随机选择m个变量，并使用对这m个变量的最佳分割来分割节点。在森林生长期间，m的值保持恒定。
  3. 每棵树都尽可能地生长。没有修剪。

入门示例

python代码实现：

代码语言：javascript复制
'''
The following code is for the Random Forest
Created by - ANALYTICS VIDHYA
'''

# importing required libraries
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')

# view the top 3 rows of the dataset
print(train_data.head(3))

# shape of the dataset
print('nShape of training data :',train_data.shape)
print('nShape of testing data :',test_data.shape)

# Now, we need to predict the missing
#  target variable in the test data
# target variable - Survived

# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']

# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']

'''

Create the object of the Random Forest model
You can also add other parameters and test your code here
Some parameters are : n_estimators and max_depth
Documentation of sklearn RandomForestClassifier: 

https://scikit-learn.org/stable/modules/
generated/sklearn.ensemble.RandomForestClassifier.html

'''
model = RandomForestClassifier()

# fit the model with the training data
model.fit(train_x,train_y)

# number of trees used
print('Number of Trees used : ', model.n_estimators)

# predict the target on the train dataset
predict_train = model.predict(train_x)
print('nTarget on train data',predict_train) 

# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('naccuracy_score on train dataset : ', accuracy_train)

# predict the target on the test dataset
predict_test = model.predict(test_x)
print('nTarget on test data',predict_test) 

# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('naccuracy_score on test dataset : ', accuracy_test)

运行结果：

代码语言：javascript复制
Shape of training data : (712, 25)

Shape of testing data : (179, 25)

Number of Trees used :  10

Target on train data [0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0
 1 0 0 0 1 0 1 0 1 1 0 1 0 1 0 0 0 0 0 0 0 1 0 1 1 1 0 0 1 0 01 0 0 0 0 0
 0 1 1 0 0 1 0 0 1 1 1 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 1 11 0 1 0 0 0
 0 0 0 1 1 0 0 1 0 1 0 1 1 0 0 0 1 0 0 1 0 0 0 1 0 1 0 1 0 0 00 1 0 1 1 0
 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 1 0 0 00 0 1 0 0 1
 0 1 1 1 1 0 0 1 0 1 0 0 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 1 1 0 00 1 0 0 0 0
 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 1 1 01 0 0 0 1 0
 0 1 1 0 1 1 1 0 1 1 0 0 1 0 0 1 1 1 1 0 0 1 0 0 0 1 1 0 0 1 10 0 0 0 0 0
 0 0 1 1 0 1 1 0 1 0 1 1 1 0 0 0 1 0 1 1 0 0 0 0 1 0 1 0 0 0 00 0 0 0 0 1
 1 0 0 1 1 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 1 0 1 00 0 0 0 1 0
 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 1 0 0 0 0 1 0 0 1 1 1 1 01 1 0 1 1 1
 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 0 1 1 0 0 1 0 1 0 1 1 11 0 0 1 0 0
 0 1 0 0 0 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 1 1 10 0 0 0 0 0
 0 0 1 1 1 0 0 1 0 1 1 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 00 0 0 1 0 1
 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 1 1 01 0 1 0 0 1
 0 0 0 1 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1 1 0 1 0 0 0 0 11 0 1 1 1 0
 1 0 1 0 1 1 0 1 0 1 0 0 1 0 0 1 0 1 1 0 1 0 0 0 1 0 1 0 0 0 00 0 0 0 0 1
 0 0 0 1 0 1 1 1 1 0 1 1 0 0 1 0 1 0 0 1 0 0 1 1 1 1 0 1 0 0 01 0 1 1 1 0
 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 00 0 1 0 1 0
 1 0 1 1 1 0 0 1 0]

accuracy_score on train dataset :  0.973314606741573

Target on test data [0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 01 1 1 1 0 0 1 0 1 1 0 1 0 1 1 0
 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 0 00 1 0 0 0 0
 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 11 0 1 1 0 1
 0 1 0 0 0 1 1 1 1 1 0 1 1 0 1 1 0 0 1 1 0 0 1 1 0 0 0 1 0 1 00 0 0 0 0 0
 0 0 0 1 1 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 0]

accuracy_score on test dataset :  0.8156424581005587
决策树 机器学习 神经网络 深度学习 人工智能

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