> 90% accuracy in my test

Looking at accuracy can be misleading if your dataset is imbalanced. Let's say 90% of your data is labelled as False and only 10% of your data is labelled as True, so even a model that doesn't look at the input at all and just predicts False all the time will have 90% accuracy. A better metric for binary classification is the F1 score, but that also depends on where you set the decision threshold (the default is 0.5, but you can change that to adjust the confusion matrix). Perhaps the most useful metric to see how much your model learned is the Area under the ROC curve aka ROC_AUC score (where 0.5 is the same as random guessing and 1 is a perfect classifier).