Can accuracy_score and precision_score be equal?
Precision = True Positive / (True Positive + False Positive)
Accuracy = (True Positive + True Negative) / (True Positive + False Positive + True Negative + False Negative)
Therefore, if there are no negative predictions, these two values will be equal.
I am trying to build a logistic regression model in python 3 using sklearn library.
Let's stick to below short versions going forward,
dv - dependent variable 
idv - independent variable
Now I have idv1, idv2, idv3, idv4, idv5, idv6, idv7, idv8 & idv9.
Out of which idv6 to idv9 are categorical variables (idv6 & idv7 has 3 categories..where as idv8 & idv9 are boolean variables..yes or no kind of variables [0 or 1])
And dv is again a boolean variable (yes or no kind of variable).
Now, I have created a dummies for all idv6 to idv9 for the final model data...i.e idv6_c1, idv6_c2, idv_c3 and followed similar for the remaining..like idv8_c1, idv8_c2 for idv8 & idv9.
Now, after fitting the model and finding the metrics of the predicted values...
I am getting let's say accuracy_score of 76.7415479670124 % and  precision_score of 76.7415479670124 %
I have calculated using sklearn.metrics.accuracy_score and sklearn.metrics.precision_score libraries.
I am wondering..is this correct or am I missing something...??
Can this happen ??...accuracy & precision to be equal to almost 13 decimals ???....I am sure...I am doing something wrong...can anyone please help me ??