机器学习-随机森林(Random Forest)

Section I: Brief Introduction on Random Forest

Random forests have gained huge popularity om applications of machine learning during the last decade due to their good classification performance,scalability, and ease of use. Intuitively, a random forest can be considered as an ensemble of decoson trees. The idea behind a random forest is to average multiple trees that individually suffer from high variance, to build a more robust model that has a better generalization performance and is less susceptible to overfitting. The major steps are summarized here:

Step 1: Draw a random boostrap sample for each decision tree with replacement Step 2: Randomly select d features without replacement.

From
Sebastian Raschka, Vahid Mirjalili. Python机器学习第二版. 南京：东南大学出版社，2018.

Section II: Random Forest import matplotlib.pyplot as plt from sklearn import datasets import numpy as np from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from DecisionTrees.visualize_test_idx import plot_decision_regions plt.rcParams['figure.dpi']=200 plt.rcParams['savefig.dpi']=200 font = {'family': 'Times New Roman', 'weight': 'light'} plt.rc("font", **font) #Section 1: Load data and split it into train/test dataset iris=datasets.load_iris() X=iris.data[:,[2,3]] y=iris.target X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=1,stratify=y) #Section 2: Invoke RandomForest model forest=RandomForestClassifier(criterion='gini', n_estimators=25, random_state=1, n_jobs=2) forest.fit(X_train,y_train) X_combined=np.vstack([X_train,X_test]) y_combined=np.hstack([y_train,y_test]) plot_decision_regions(X=X_combined, y=y_combined, classifier=forest, test_idx=range(105,150)) plt.xlabel('petal length [standardized]') plt.ylabel('petal width [standardized]') plt.legend(loc='upper left') plt.savefig('./fig3.png') plt.show()

参考文献：
Sebastian Raschka, Vahid Mirjalili. Python机器学习第二版. 南京：东南大学出版社，2018.

作者：Santorinisu

forest 随机森林 学习 random 机器学习