 In the previous tutorial, we've made some predictions on fruits and vegetables. But you've been probably wondering how do we know which classification model works best? Today I'll show you how to assess the quality of various prediction methods. This time, we're going back to our good old Iris dataset. First, we will train a model with one of the simplest classification techniques, a logistic regression. To avoid overfitting, we are first building the model on the training data and then testing it on a separate test data. We want to repeat this procedure multiple times and then report on average accuracy. This is what cross-validation does and it can be found in the test and score widget. In our case, cross-validation splits the data into 10 subsets, uses 9 for training the model and the remaining subset for testing it. It repeats this procedure 9 more times, each time using a different subset for testing. Cross-validation results are reported on our right. What do these numbers mean? The simplest one to understand is classification accuracy in the second column. It reports on the proportion of correctly classified data instances. Looks like classification accuracy was 96%. So what are the 4% of the data that were misclassified? We can check this with confusion matrix. Looks like our model had no problems with classifying irisetosis. Still, it got a bit confused with fussycolors and virginicus. We can easily observe misclassifications by selecting misclassified instances and sending them to the data table. Just by looking at this table, it is still hard to interpret the results and say much about misclassifications. Instead, let's visualize them in scatterplot. Most misclassifications are at the border area between the two classes, the red for iris vesicala and the green for iris virginica. We expected something like this, right? Logistic regression is of course not the only classification method we can apply on iris. We could use, say, random forest. This is a more complex classification technique, but often has better accuracy. However, not in this case, where the accuracy of logistic regression is already quite high. Besides accuracy, test and score widget reports on 4 other scores. We really like the area under receiver operating characteristics, or AUC in short. But that will be the topic of some other tutorial. We can also check where misclassifications lie for random forest. But I'll leave this to you. Today we've learned how to use cross-validation in orange and how to compare several classifiers on a single dataset. I've also showed you how to select misclassifications and visualize them.