The footer for this table shows one of these, McFadden's rho-squared. A variety of pseudo r-squared statistics are used instead. This is interpreted in exactly the same way as with the r-squared in linear regression, and it tells us that this model only explains 19% of the variation in churning.ДЄlthough the r-squared is a valid computation for logistic regression, it is not widely used as there are a variety of situations where better models can have lower r-squared statistics. The footer of the table below shows that the r-squared for the model is 0.1898. Typically we would expect to see a lower prediction accuracy when assessed out-of-sample - often substantially lower. The good news here is that in this case the prediction accuracy has improved a smidge to 79.1%. The table below shows the prediction accuracy of the model when applied to 1,761 observations that were not used when fitting the logistic regression. This tests whether the accuracy of the model is likely to hold up when used in the 'real world'. A more thorough way of assessing prediction accuracy is to perform the calculation using data not used to create the model. The accuracy discussed above is computed based on the same data that is used to fit the model. It is for this reason that you need to be sceptical when people try and impress you with the accuracy of predictive models when predicting a rare outcome it is easy to have a model that predicts accurately (by making it always predict against the rare outcome). If the groups being predicted are not of equal size, the model can get away with just predicting people are in the larger category, so it is always important to check the accuracy separately for each of the groups being predicted (i.e., in this case, churners and non-churners). But, it is important to keep this in mind when evaluating the accuracy of any predictive model. There is nothing wrong with the model doing this. What's going on here? As most people did not churn, the model is able to get some easy wins by defaulting to predicting that people do not churn. However, if you sum up the first column, you can see that the model has predicted that 2,765 people did not churn. If you sum up the totals of the first row, you can see that 2,575 people did not churn. So, among people who did churn, the model only correctly predicts that they churned 51% of the time. It shows us that among people who did churn, the model was only marginally more likely to predict they churned than did not churn (i.e., 483 versus 464). If you hover your mouse over each of the cells of the table you see additional information, which computes a percentage telling us that the model accurately predicted non-churn for 83% of those that did not churn. Starting with the NoВ row of the table, we can see that the there were 2,301 people who did not churn and were correctly predicted not to have churned, whereas only 274 people who did not churn were predicted to have churned. In this case 79.05% is not quite as good as it might sound. Is this a good result? The answer depends a bit on context. 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. At the base of the table you can see the percentage of correct predictions is 79.05%.

The table below shows the prediction-accuracy tableВ produced by Displayr's logistic regression. To understand this we need to look at the prediction-accuracy tableВ (also known as the classification table, hit-miss table,В and confusion matrix).

The most basic diagnostic of a logistic regression is predictive accuracy.

Binary logistic regression spss