GridSearchCV与RandomizedSearchCV

RandomizedSearchCV使用案例：

#Loading libraries
import pandas as pd
import numpy as np
pd.options.display.max_rows = 999
pd.options.display.max_columns = 999
from sklearn.model_selection import train_test_split
import lightgbm as lgb
from sklearn.model_selection import RandomizedSearchCV
from sklearn import model_selection, preprocessing, metrics
import matplotlib.pyplot as plt
import os#inputting parameters
train = pd.read_csv("../input/train.csv")
test = pd.read_csv("../input/train.csv")#train test split
X_train,X_test,y_train,y_test = train_test_split(train.drop(["target","ID_code"],axis=1),train["target"],test_size=0.3,random_state=14)#grid of parameters
gridParams = {'learning_rate': [0.05],'num_leaves': [90,200],'boosting_type' : ['gbdt'],'objective' : ['binary'],'max_depth' : [5,6,7,8],'random_state' : [501], 'colsample_bytree' : [0.5,0.7],'subsample' : [0.5,0.7],'min_split_gain' : [0.01],'min_data_in_leaf':[10],'metric':['auc']}#modelling
clf = lgb.LGBMRegressor()
grid = RandomizedSearchCV(clf,gridParams,verbose=1,cv=10,n_jobs = -1,n_iter=10)
grid.fit(X_train,y_train)
#best parameters
grid.best_params_

GridSearchCV使用案例：

from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
iris = datasets.load_iris()
parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
svc = svm.SVC(gamma="scale")
clf = GridSearchCV(svc, parameters, cv=5)
clf.fit(iris.data, iris.target)

GridSearchCV与RandomizedSearchCV相关推荐

GridSearchCV和RandomizedSearchCV调参
1 GridSearchCV实际上可以看做是for循环输入一组参数后再比较哪种情况下最优. 使用GirdSearchCV模板 # Use scikit-learn to grid search the ...
使用GridSearchCV和RandomizedSearchCV进行超参数调整
In Machine Learning, a hyperparameter is a parameter whose value is used to control the learning pro ...
GridSearchCV和RandomizedSearchCV（以SVM为例）
GridSearchCV参考代码 CC = []gammas = []for i in range(-5, 16, 2):CC.append(2 ** i)for i in range(3, -16, ...
暴力调参——GridSearchCV、RandomizedSearchCV、贝叶斯优化
https://blog.csdn.net/juezhanangle/article/details/80051256 http://www.360doc.com/content/18/0707/15 ...
GridSearchCV 与 RandomizedSearchCV 用法注意随机搜索可以自定义打分 or loss 函数
在一些情况下,sklearn中没有现成的评价函数,sklearn是允许我们自己的定义的,但需要注意格式,接下来给个例子 import numpy as np from sklearn.metrics ...
GridSearchCV 与 RandomizedSearchCV 调参
https://blog.csdn.net/juezhanangle/article/details/80051256
【小白学习keras教程】十、三种Model Selection方法：k-fold cross-validation，GridSearchCV和RandomizedSearchCV
@Author:Runsen Model Selection是划分训练集和测试集的手段,下面总结了三种Model Selection方法. k-fold cross-validation using ...
Lesson 6.5Lesson 6.6.1Lesson 6.6.2 机器学习调参基础理论与网格搜索多分类评估指标的macro与weighted过程GridSearchCV的进阶使用方法
Lesson 6.5 机器学习调参基础理论与网格搜索在上一小节执行完手动调参之后,接下来我们重点讨论关于机器学习调参的理论基础,并且介绍sklearn中调参的核心工具--GridSearchCV. ...
Scikit-learn 更新至 0.24 版，这 10 个新特性你需要了解
点击上方"视学算法",选择加"星标"或"置顶" 重磅干货,第一时间送达转自 | towardsdatascience 作者 | Jeff ...

GridSearchCV与RandomizedSearchCV

GridSearchCV与RandomizedSearchCV相关推荐

最新文章

热门文章