题目内容

下面的一段python程序的目的是利用皮尔逊相关系数进行iris数据集特征选择import numpy as npfrom scipy.stats import pearsonrfrom sklearn import datasetsiris = datasets.load_iris()print ("Pearson's correlation coefficient between column #1 and target column", pearsonr(iris.data[:,0], iris.target ))print ("Pearson's correlation coefficient between column #2 and target column", pearsonr(iris.data[:,1], iris.target ))print ("Pearson's correlation coefficient between column #3 and target column", pearsonr(iris.data[:,2], iris.target ))print ("Pearson's correlation coefficient between column #4 and target column", pearsonr(iris.data[:,3], iris.target )) 其输出结果为：("Pearson's correlation coefficient between column #1 and target column", (0.7825612318100814, 2.890478352614054e-32))("Pearson's correlation coefficient between column #2 and target column", (-0.4194462002600275, 9.159984972550002e-08))("Pearson's correlation coefficient between column #3 and target column", (0.9490425448523336, 4.1554775794971695e-76))("Pearson's correlation coefficient between column #4 and target column", (0.9564638238016173, 4.775002368756619e-81)) 则如果去掉一个特征，应该选择哪一个特征？

A. #1
B. #2
C. #3
D. #4

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下面的一段python程序的目的是对样本特征矩阵进行归一化处理，则空格处应该填充的函数是？ from sklearn import datasetsiris = datasets.load_iris()from sklearn.preprocessing import Normalizerprint (Normalizer(norm='l1'). (iris.data))

A. fit
B. fit_transform
C. transform
D. normalizer

下面的一段python程序的目的是使用主成分分析法（principal component analysis）对iris数据集进行特征降维，以便于数据的二维平面可视化。则其中空格处应该填充的数字为？ import matplotlib.pyplot as pltfrom sklearn.decomposition import PCAfrom sklearn.datasets import load_irisdata = load_iris()X = data.datay = data.targetpca = PCA(n_components= )reduced_X = pca.fit_transform(X)

A. 1
B. 2
C. 3
D. 4

下面的一段python程序的目的是使用区间缩放法对矩阵的列数据进行量纲缩放，则两处空格应该分别填入什么？ from sklearn.preprocessing import MinMaxScalerdata = [[0, 0], [0, 78], [80, 1], [100, 89]] scaler = MinMaxScaler()scaler. (data)results=scaler. (data)print (results)

A. fit， fit
B. transform， transform
C. transform， fit
D. fit， transform

下面的一段python程序的目的什么?import scipy.sparse as spfrom sklearn.preprocessing import Imputerx = sp.csc_matrix([ [1, 2], [0, 3], [7, 6]])imp = Imputer(missing_values=0, strategy='mean', verbose=0)imp.fit(x)x_test = sp.csc_matrix([ [0, 2], [6, 0], [7, 6]])

A. 样本特征矩阵的量纲的缩放
B. 缺失值补齐，将0视为缺失值
C. 样本特征矩阵的归一化
D. 多项式特征的生成