python实现随机森林（RF）的参数寻优

最新推荐文章于 2025-05-14 19:42:36 发布

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最新推荐文章于 2025-05-14 19:42:36 发布

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分类专栏：机器学习类

本文链接：https://blog.youkuaiyun.com/super_he_pi/article/details/85075826

这篇博客介绍了如何使用Python进行随机森林（RF）的参数调优。首先，将原始数据划分为训练集和验证集，然后通过网格搜索方法以准确率（ACC）作为评估标准，优化随机森林的参数。

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# -*- coding: utf-8 -*-
#RandomForestClassifier
import math
import matplotlib as mpl
import warnings
import numpy as np
from sklearn import tree
from sklearn import ensemble
from sklearn import metrics
from sklearn.metrics import auc 
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import classification_report
from sklearn.cross_validation import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.externals import joblib
import matplotlib.pyplot as plt
plt.switch_backend('agg') 
warnings.filterwarnings('ignore')


# 数据读入,划分训练与验证 
def split_data(file_name,N):  #uniprot_10_1_1_lst.txt
	data = []
	labels = []
	with open(file_name,'r') as ifile:   #-*-# file's name #-*-#
		for line in ifile:
			toke