ROC

Before you begin any Analysis, its best to consult the concerned Business Expert and know what type of Error that you see in the Confusion Matrix is of more concern for the Business Problem.

Ex- It may be the case that False Negatives are probably worse than False Positives to your Business.

Remember, that a Confusion Matrix that the ML model is usually calculated for a Threshold Value of 0.5, a default value in most of the Predictive Analytics Tools in the market.  Nevertheless, you can always tune the parameters to arrive at the Best Results. You can plot an ROC curve for the model to visualize the performance of the model across all the the possible different Threshold values. One point on an ROC curve corresponds to just one threshold used to calculate the values of the Confusion Marix.

Knowing the AUC of the ROC curve can help you compare different models. Higher AUC is desirable. When the curves do not cross each other, its easy to judge. It implies, One model is outperforming the other model against all the Threshold values. In such a case, you pick the model with Higher AUC.

But AUC alone doesn't tell you the entire story. The shape of the ROC curve matters too when you are comparing two models, apart from its AUC. It needs closer inspection than AUC, when the ROC curves of two models crosses each other. At this point you need to make a choice, High Precision/Low Recall or Low Precision/High Recall. Notice, that at the end of the day, you are interested in picking one threshold for your classification. When the ROC curves  it may happen that a model with relatively less AUC value could be of more significance to you.