X_test_with_nb_pred_proba (0, 566)0 (0, 2642)0 (0, 3256)0 (0, 3376)0 (0, 6006)0 (0, 6778)0 (0, 6853)0 (0, 6868)0 (0, 8486)0 (0, 10339)0 (0, 10985)0 (0, 11056)0 (0, 11061)0 (0, 11107)0 (0, 11117)0 (0, 11131)1 (0, 11200)1 (0, 11307)0 (0, 12145)0 (0, 14880)0 (0, 15759)2 (0, 19143)0 (0, 19781)0 (0, 19832)2 (0, 24705)0 … (1870, 53405)1 (1870, 54234)1 (1870, 57675)1 (1870, 58023)1
It looks like you’ve provided a sparse matrix representation where each row represents a document, and the columns represent different features (presumably words or tokens). The values in the matrix seem to represent the frequency or presence of each feature in the respective document.
The format (i, j) v indicates that in row i and column j, the value is v. For instance, (0, 566) 0 means that in the first document (index 0), the feature represented by column 566 has a value of 0.
If you have any specific questions or if you need further assistance, feel free to ask!