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What is the accuracy of the logistic regression model?
Prediction accuracy At the base of the table you can see the percentage of correct predictions is 79.05%. This tells us that for the 3,522 observations (people) used in the model, the model correctly predicted whether or not somebody churned 79.05% of the time.
What is random state in logistic regression?
Random state ensures that the splits that you generate are reproducible. Scikit-learn uses random permutations to generate the splits. The random state that you provide is used as a seed to the random number generator. This ensures that the random numbers are generated in the same order.
What does percentage of correct predictions in logistic regression mean?
At the base of the table you can see the percentage of correct predictions is 79.05%. This tells us that for the 3,522 observations (people) used in the model, the model correctly predicted whether or not somebody churned 79.05% of the time. Is this a good result? The answer depends a bit on context.
Which is better logistic regression or are squared?
Like r-squared statistics, these statistics are guaranteed to take values from 0 to 1, where a higher value indicates a better model. The reason that they are preferred over traditional r-squared is that they are guaranteed to get higher as the fit of the model improves.
How to interpret logistic regression outputs you displayr?
To understand this we need to look at the prediction-accuracy table (also known as the classification table, hit-miss table, and confusion matrix ). The table below shows the prediction-accuracy table produced by Displayr’s logistic regression. At the base of the table you can see the percentage of correct predictions is 79.05%.
How is AUC used to validate logistic regression?
A measure that is often used to validate logistic regression, is the AUC of the ROC curve (plot of sensitivity against 1-specificity – just google for the terms if needed). This, in essence, evaluates the whole range of threshold values.