Need to convert all variables to numeric data type before using LogisticRegressionwithp-values class
cols_to_convert = [
'grade:A',
'grade:B',
'grade:C',
'grade:D',
'grade:E',
'grade:F',
'grade:G',
'home_ownership:RENT_OTHER_NONE_ANY',
'home_ownership:OWN',
'home_ownership:MORTGAGE',
'addr_state:ND_NE_IA_NV_FL_HI_AL',
'addr_state:NM_VA',
'addr_state:NY',
'addr_state:OK_TN_MO_LA_MD_NC',
'addr_state:CA',
'addr_state:UT_KY_AZ_NJ',
'addr_state:AR_MI_PA_OH_MN',
'addr_state:RI_MA_DE_SD_IN',
'addr_state:GA_WA_OR',
'addr_state:WI_MT',
'addr_state:TX',
'addr_state:IL_CT',
'addr_state:KS_SC_CO_VT_AK_MS',
'addr_state:WV_NH_WY_DC_ME_ID',
'verification_status:Not Verified',
'verification_status:Source Verified',
'verification_status:Verified',
'purpose:educ__sm_b__wedd__ren_en__mov__house',
'purpose:credit_card',
'purpose:debt_consolidation',
'purpose:oth__med__vacation',
'purpose:major_purch__car__home_impr',
'initial_list_status:f',
'initial_list_status:w',
'term:36',
'term:60',
'emp_length:0',
'emp_length:1',
'emp_length:2-4',
'emp_length:5-6',
'emp_length:7-9',
'emp_length:10',
'mths_since_issue_d:<38',
'mths_since_issue_d:38-39',
'mths_since_issue_d:40-41',
'mths_since_issue_d:42-48',
'mths_since_issue_d:49-52',
'mths_since_issue_d:53-64',
'mths_since_issue_d:65-84',
'mths_since_issue_d:>84',
'int_rate:<9.548',
'int_rate:9.548-12.025',
'int_rate:12.025-15.74',
'int_rate:15.74-20.281',
'int_rate:>20.281',
'mths_since_earliest_cr_line:<140',
'mths_since_earliest_cr_line:141-164',
'mths_since_earliest_cr_line:165-247',
'mths_since_earliest_cr_line:248-270',
'mths_since_earliest_cr_line:271-352',
'mths_since_earliest_cr_line:>352',
'delinq_2yrs:0',
'delinq_2yrs:1-3',
'delinq_2yrs:>=4',
'inq_last_6mths:0',
'inq_last_6mths:1-2',
'inq_last_6mths:3-6',
'inq_last_6mths:>6',
'open_acc:0',
'open_acc:1-3',
'open_acc:4-12',
'open_acc:13-17',
'open_acc:18-22',
'open_acc:23-25',
'open_acc:26-30',
'open_acc:>=31',
'pub_rec:0-2',
'pub_rec:3-4',
'pub_rec:>=5',
'total_acc:<=27',
'total_acc:28-51',
'total_acc:>=52',
'acc_now_delinq:0',
'acc_now_delinq:>=1',
'total_rev_hi_lim:<=5K',
'total_rev_hi_lim:5K-10K',
'total_rev_hi_lim:10K-20K',
'total_rev_hi_lim:20K-30K',
'total_rev_hi_lim:30K-40K',
'total_rev_hi_lim:40K-55K',
'total_rev_hi_lim:55K-95K',
'total_rev_hi_lim:>95K',
'annual_inc:<20K',
'annual_inc:20K-30K',
'annual_inc:30K-40K',
'annual_inc:40K-50K',
'annual_inc:50K-60K',
'annual_inc:60K-70K',
'annual_inc:70K-80K',
'annual_inc:80K-90K',
'annual_inc:90K-100K',
'annual_inc:100K-120K',
'annual_inc:120K-140K',
'annual_inc:>140K',
'dti:<=1.4',
'dti:1.4-3.5',
'dti:3.5-7.7',
'dti:7.7-10.5',
'dti:10.5-16.1',
'dti:16.1-20.3',
'dti:20.3-21.7',
'dti:21.7-22.4',
'dti:22.4-35',
'dti:>35',
'mths_since_last_delinq:Missing',
'mths_since_last_delinq:0-3',
'mths_since_last_delinq:4-30',
'mths_since_last_delinq:31-56',
'mths_since_last_delinq:>=57',
'mths_since_last_record:Missing',
'mths_since_last_record:0-2',
'mths_since_last_record:3-20',
'mths_since_last_record:21-31',
'mths_since_last_record:32-80',
'mths_since_last_record:81-86',
'mths_since_last_record:>86',
]
loan_data_inputs_train[cols_to_convert] = loan_data_inputs_train[cols_to_convert].astype('float64')
loan_data_inputs_test[cols_to_convert] = loan_data_inputs_test[cols_to_convert].astype('float64')
2 answers ( 0 marked as helpful)
I dont understand there are some variables with dtype = boolean, so it cannot run the reg.fit with p-values because the formula for F_ij requires all variables to be numeric data type:
F_ij = np.dot((X / denom).T,X) ## Fisher Information MatrixThis code is in class LogisticRegression_with_p_values
In my case it still giving me an error, but now another one:
LinAlgError: Singular matrix
This came when trying:
---> 20 Cramer_Rao = np.linalg.inv(F_ij) ## Inverse Information Matrix
Any idea about how to do?
LinAlgError: Singular matrix
This came when trying:
---> 20 Cramer_Rao = np.linalg.inv(F_ij) ## Inverse Information Matrix
Any idea about how to do?
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