sklearn model_selection to obtain five-fold cross-validation score as an estimate of the probability that the predicted class matches the expert' class consider the credit approval data set crx data from uci' credit approval website the data set is concerned with credit card applications the last column in the data set indicates whether the application is approved (+or not (-with the view of preserving data privacyall explanatory variables were anonymized note that some explanatory variables are continuous and some are categorical (aload and prepare the data for analysis with sklearn firsteliminate data rows with missing values nextencode categorical explanatory variables using onehotencoder object from sklearn preprocessing to create model matrix with indicator variables for the categorical variablesas described in section (bthe model matrix should contain rows and columns the response variable should be / variable (reject/approvewe will consider several classification algorithms and test their performance (using zero-one lossvia ten-fold cross validation write function which takes parametersxyand modeland returns the ten-fold cross-validation estimate of the expected generalization risk ii consider the following sklearn classifierskneighborsclassifier ( )logisticregressionand mplclassifier (multilayer perceptronuse the function from (ito identify the best performing classifier consider synthetic data set that was generated in the following fashion the explanatory variable follows standard normal distribution the response label is if the explanatory variable is between the and quantiles of the standard normal distributionand otherwise the data set was generated using the following code import numpy as np import scipy stats generate data np random seed ( np random randn (nq scipy stats norm ppf ( np zeros (ny[ >= [ <=- reshape - , compare the -nearest neighbors classifier with and logistic regression classifier without computationwhich classifier is likely to be better for these dataverify your answer by coding both classifiers and printing the corresponding training - loss consider the digits data set from exercise in this exercisewe would like to train binary classifier for the identification of digit (adivide the data such that the first rows are used as the training set and the rest are used as the test set