How to correctly interpret the table function to determine logistic regression accuracy?
The outcome of a multi-nomial or binomial is confusion matrix (2*2 for binomial, n*n for multinomial),

Interpretation of the confusion matrix is for example:



Accuracy of the model : ((TP + TN) / (TP + FN + FP + TN) ) * 100
It means you model could exactly pick this much percent of data was classified correctly.

Precision: (TP / (TP + FP))*100
That measure of correctness achieved in positive prediction i.e. of observations labeled as positive, how many are actually labeled positive

Recall: (TP / (TP + FN)) * 100
It is a measure of actual observations which are labeled (predicted) correctly i.e. how many observations of positive class are labeled correctly. It is also known as ‘Sensitivity’.

F-Measure: ((1 + β)² × Recall × Precision) / ( β² × Recall + Precision )
It combines precision and recall as a measure of effectiveness of classification in terms of ratio of weighted importance on either recall or precision as determined by β coefficient.
generally β is 1.

If you want read them in just normal words in you example:

        Predicted  
          M   F
       M 100 150
actual F 100 300


It means your model could predict 100+300 correctly out of 100+150+100+300

Actual number of males where 250 but you could classify 100 correctly and 50 wrongly.

Similarly, Actual number of females where 400 but you could classify 300 correctly and 100 wrongly.

If you any need more detail let me know.

Go through this Link, you will get better idea.
This Link is an interactive chart, which will give you better understanding.