We'll need a target variable to predict the output with the actual values if there is no output column you can just create one
In case of R use the code: test_set$Output_Column_Name<- NA
In case of Python use the code: test_set['Output_Column_Name'] = np.nan
Up next after creating of the target column, you'll have to predict the output on it using the logistic regression model created.
Once you have the target column values which would be binary in nature i.e. either YES/NO or 1 / 0, we'll compare it with the actual values based upon a confusion matrix.
Confusion matrix takes into account the below mentioned values
- True Positive: number of instances in which the actual and the predicted value both are True
- True Negative: number of instances in which the actual and the predicted value both are False
- False Positive: number of instances in which the actual is False but the predicted value is True
- False Negative: number of instances in which the actual is True but the predicted value is False
Based on the actual number of above values the accuracy of the model is created as per the formula
(True Positive +True Negatve) / Total number of Instances
For more details about the confusion matrix you can refer to the following link:https://bit.ly/2RSDtW8
Hope this helps :)