from sklearn.model_selection import cross_val_score #使用K折交叉验证模块,cross validation 有5组(cv=5),或者说是5个set。 scores = cross_val_score(knn, X, y, cv=5, scoring='accuracy') #将5次的预测准确率打印出 print(scores) # [ 0.96666667
model_selection import train_test_split, cross_val_score, GridSearchCV, etc. 这里列举了model_selection模块中的一些常用函数,如train_test_split用于划分训练集和测试集,cross_val_score用于计算交叉验证得分,GridSearchCV用于参数调优等。 替换函数名: 在大多数情况下,model_selection模块中的函数与cross_validat...
sklearn.cross_validation.cross_val_score(estimator, X, y=None, scoring=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch=‘2*n_jobs’) 参数 estimator:数据对象 X:数据 y:预测数据 soring:调用的方法 cv:交叉验证生成器或可迭代的次数 n_jobs:同时工作的cpu个数(-1代表全部)...
1.1 API接口 sklearn.cross_validation.cross_val_score(estimator, X, y=None, scoring=None,cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch=‘2*n_jobs’) 1. 1.2 API接口参数 estimator:估计方法对象(分类器),模型X:数据特征(Features) y:数据标签(Labels) soring:调用方法(包括accurac...
cv:int, cross-validation generator or an iterable, default=None 我们要进行的交叉验证的方法 几个常用的参数如上所示,下面,我们举几个小栗子。 KFold 关于KFold参考: 橘猫吃不胖:sklearn函数:KFold(分割训练集和测试集)62 赞同 · 4 评论文章 对于K折交叉验证, K的取值,通常为3、5、10 import pandas...
scores= cross_val_score(knn,iris_X,iris_Y,cv=5,scoring="accuracy")print(scores.mean()) importnumpy as npfromsklearnimportdatasetsfromsklearn.cross_validationimporttrain_test_splitfromsklearn.neighborsimportKNeighborsClassifierfromsklearn.cross_validationimportcross_val_scoreimportmatplotlib.pyplot as pl...
from sklearn.cross_validation import cross_val_score import time from sklearn.datasets import load_iris iris = load_iris() models = [GaussianNB(), DecisionTreeClassifier(), SVC()] names = ["Naive Bayes", "Decision Tree", "SVM"]
We've looked at the using cross validation iterators that scikit-learn comes with, but we can ...
1、sklearn.cross_validation模块 (1)sklearn.cross_validation.cross_val_score()函数:返回交叉验证后得到的分类率。 详情见http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.cross_val_score.html sklearn.cross_validation.cross_val_score(estimator, X, y=None, scoring=None, cv=...
2.cross_val_score 对数据集进行指定次数的交叉验证并为每次验证效果评测 调用方式: sklearn.cross_validation.cross_val_score(estimator, X, y=None, scoring=None, cv=None,n_jobs=1, verbose=0, fit_params=None, pre_dispatch='2*n_jobs') ...