例子: >>>fromsklearnimportdatasets, linear_model>>>fromsklearn.model_selectionimportcross_val_score>>>diabetes = datasets.load_diabetes()>>>X = diabetes.data[:150]>>>y = diabetes.target[:150]>>>lasso = linear_model.Lasso()>>>print(cross_val_score(lasso, X, y, cv=3)) [0.331507340...
from sklearn import datasets #自带数据集 from sklearn.model_selection import train_test_split,cross_val_score #划分数据 交叉验证 from sklearn.neighbors import KNeighborsClassifier #一个简单的模型,只有K一个参数,类似K-means import matplotlib.pyplot as plt iris = datasets.load_iris() #加载sklearn...
cross_val_score(clf, iris.data, iris.target, cv=custom_cv) array([1. , 0.97333333]) 保留数据的数据转换 正如在训练集中保留的数据上测试一个predictor(预测器)是很重要的一样,预处理(标准化、特征选择等)和类似的 data transformations也可以从训练集中学习,并应用预测数据以进行预测: from sklearn impor...
41 -- 7:05 App sklearn16:cross_val_score and GridSearchCV 89 -- 3:28 App sklearn1:ColumnTransformer是个好东西 28 -- 4:40 App sklearn15:不要用drop='first' with OneHotEncoder 128 -- 3:43 App sklearn32:多分类 AUC 56 -- 2:41 App sklearn27:类别特征的缺失值处理 2473 ...
import numpy as np from sklearn.model_selection import train_test_split from sklearn import svm from sklearn.model_selection import cross_val_score target=odata["target"] X=odata.drop(columns="target") clf = svm.SVC(kernel='linear', C=1) ...
from sklearn import svm from sklearn.datasets import load_iris from sklearn.model_selection import cross_val_score iris = load_iris() svc = svm.SVC() scores = cross_val_score(svc, iris.data, iris.target, cv=5) print(scores)
values[:,8] from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score from sklearn.linear_model import LogisticRegression kfold = KFold(n_splits=10) model = LogisticRegression() result = cross_val_score(model , X , y , cv=kfold) 最后的result,就是...
简介:sklearn中的cross_val_score交叉验证 cross_val_score交叉验证 1.0 注意事项 1. 参数cv代表分成几折,其中cv-1折用于训练,1折用于测试 2. cv数值最大 = 数据集总量的1/33. 关于参数scoring:1. ‘accuracy’:准确度;2. ‘f1’:F1值,只用于二分类; ...
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 ...
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)...