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收集整理的這篇文章主要介紹了
GridSearchCV与RandomizedSearchCV
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RandomizedSearchCV使用案例：
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#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)
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