Language Identification of Bengali-English Code-Mixed data using Character & Phonetic based LSTM Models
Table 5: Confusion matrices. (1-character, 2-phonetic, 3-ensem_stack, 4-ensem_thresh)
['[BOLD] Confusion Matrices 1', '[BOLD] Confusion Matrices  [ITALIC] BN', '[BOLD] Confusion Matrices  [ITALIC] EN', '[BOLD] Confusion Matrices 2', '[BOLD] Confusion Matrices  [ITALIC] BN', '[BOLD] Confusion Matrices  [ITALIC] EN']
We started our error analysis by preparing confusion matrices (CM) of the four models. Here, BN correctly predicted was considered as TP and EN was considered as TN. Unlike the character model, we can see from CM 2 (phonetic model) that TP is much more than TN, thus the precision is higher by 0.15% compared to the character model. From CM 3, we observed that TN is much higher compared to TP (by 39) and thus, it can be inferred that the logistic regression model is biased towards EN. On the contrary, from CM 4, we can claim that TP is much higher compared to TN (by 41). This is due to the fact that the regression range of the fuzzy outputs from the models for BN is much higher compared to that of EN, and thus calculating threshold keeping accuracy as the metric favors TP.