#StratifiedKFold#相比于KFold,在进行split的时候需要传入y,并且会根据y的分类,保证分类后y在各个数据集中比例不变,类似于GroupKFold(基于参数groups)importnumpy as npimportpandas as pdfromsklearn.model_selectionimport*fromsklearn.datasetsimportmake_classification SEED= 666 X,y= make_...
plt.yticks(locs,list(map(lambdax:"%g"%x,locs))) plt.ylabel("CV score") plt.xlabel("Parameter C") plt.ylim(0,1.1) plt.show() 结果图 ②对每个输入数据点产生交叉验证估计:model_selection.cross_val_predict(estimator,X) fromsklearnimportdatasets,linear_modelfromsklearn.model_selectionimportcros...
credit[col] = credit[col].map(lambda x:x.strip()) credit[col] = credit[col].map(col_dicts[col]) #划分数据集:30%测试集,70%训练集 y = credit['default'] X = credit.loc[:,'account_check_status':'foreign_worker'] X_train, X_test, y_train, y_test = model_selection.train_test...
from sklearn.model_selection import train_test_split train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0) # Define model melbourne_model = DecisionTreeRegressor() # Fit model melbourne_model.fit(train_X, train_y) # get predicted prices on validation data val_...
在这里,我们使用SKlearn构建三种不同分布的数据,然后在这些数据集上测试一下决策树的效果,让大家更好地理解决策树。下图就是三种表现结果,后面会详细介绍实现过程~ 1. 导入需要的库 import numpy as npimport matplotlib.pyplot as pltfrom matplotlib.colors import ListedColormapfrom sklearn.model_selection import...
import pandas as pd import numpy as np from sklearn.model_selection import train_test_split 1. 2. 3. 导入数据,探索数据 weather = pd.read_csv(r"C:\work\learnbetter\micro-class\week 8SVM (2)\data\weatherAUS5000.csv",index_col=0) weather.head() 1. 2. 来查看一下各个特征都代表了什...
import matplotlib.pyplot as pltimport seaborn as snsfrom sklearn.datasets import load_winefrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerfrom sklearn.neighbors import KNeighborsCla...
OneHotEncoderfrom sklearn.preprocessing import FunctionTransformerfrom sklearn.preprocessing import Binarizerfrom sklearn.preprocessing import MinMaxScalerfrom sklearn.feature_selection import SelectKBestfrom sklearn.feature_selection import chi2from sklearn.decomposition import PCAfrom sklearn.linear_model ...
二、交叉验证岭回归 --- klearn.linear_model.RidgeCV 加利福尼亚房屋价值测试 一、岭迹图 既然要选择α的范围,我们就不可避免地要进行α最优参数的选择。在各种机器学习教材中,都会使用岭迹图来判断正则项参数的最佳取值。传统的岭迹图长这样,形似一个开口的喇叭...
fromsklearn.model_selectionimporttrain_test_split## 这里 X 和 y 上面我们已经准备好了Xtrain,Xtest,ytrain,ytest=train_test_split(X,y,random_state=1898)Xtrain.shapeXtest.shapeytrain.shapeytest.shape(112,4)(38,4)(112,)(38,) 第二步,导入模型的类; ...