What model evaluation metrics can be used for logistic regression and how do I implement them? I know with linear regression I can use P-Values, but that does not appear to be applicable with logistic regression.

[70] : Logit Regression Results Dep. Variable: ReAdmis No. Observations: 7500 Model: Logit Df Residuals: 7482 Method: MLE Df Model: 17 Date: Sun, 11 Sep 2022 Pseudo R-squ.: 0.001185 Time: 13:01:30 Log-Likelihood: -4919.6 converged: True LL-Null: -4925.4 Covariance Type: nonrobust LLR p-value: 0.8194 coef std err Z P>|ZI [0.025 0.975] const -0.4490 0.328 -1.369 0.171 -1.092 0.194 Income -1.008e-06 8.48e-07 -1.189 0.235 -2.67e-06 6.54e-07 VitD_levels 0.0045 0.012 0.378 0.705 -0.019 0.028 Doc_visits 0.0103 0.023 0.449 0.654 -0.035 0.056 Full_meals_eaten -0.0133 0.024 -0.553 0.580 -0.060 0.034 vitD_supp 0.0470 0.038 1.238 0.216 -0.027 0.121 Soft_drink 0.0193 0.055 0.352 0.725 -0.088 0.127 Stroke 0.0056 0.060 0.093 0.926 -0.112 0.123 Overweight -0.0385 0.053 -0.728 0.466 -0.142 0.065 Asthma -0.0595 0.053 -1.125 0.260 -0.163 0.044 Timely_admission -0.0301 0.035 -0.872 0.383 -0.098 0.038 Timely_treatment 0.0447 0.032 1.409 0.159 -0.017 0.107 Timely_visits -0.0184 0.029 -0.625 0.532 -0.076 0.039 Reliability 0.0067 0.026 0.254 0.800 -0.045 0.058 Options -0.0013 0.028 -0.046 0.963 -0.055 0.053