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Do you get the same result when you run your code again, generating a new random split between training and test data? It grows with train accuracy until a certain depth around 5 , then starts deviating down as expected - however, surprisingly, it does not fall as much as could be expected due to overfitting, remaining rather flat for depths greater than 8.

Let's see what happens when we do lots of random splits, and allow more depth! K-fold cross-validation works by splitting X into K partitions of equal length. Then in each fold, you choose K-1 partitions as the training set and the remaining partition as the test set.

Every partition gets a turn being a test set. Finally, compute the average classification errors on the test- and training sets across the 10 cross-validation folds and plot them as a function of the tree depth. Those indices can then be used to create the training and test set, e. How about fold cross-validation? It describes all you need to know and much, much more..

The method explained for computing the area under the curve is unnecessarily complicated. This is then divided by the total number of comparisons between probabilities. Do you think this classifier works well? I guess it depends on the domain of application. The first column gives the true class label either 0 or 1.

The second and third column give the probabilistic scores for two different classifiers. The higher this probability, the more certain the classifier is that the example belongs to class 1 instead of class 0. Make the plot yourself as opposed to using a library! Also plot a dashed line on the diagonal to represent random guessing in the same figure. Interpret the obtained results. Do both classifiers perform better than random guessing? Add Markdown cells where necessary. This contains the same data as used in the earlier assignment, but with outliers and the 12th attribute already removed.

Attribute names and class names are stored in the attributeNames and classNames objects, which contain arrays, of which the first element contains the names. To get the names from those arrays, you can use list comprehension or for-loops. Use the Gini gdi splitting criterion. Have another look at the visualized tree.

Which attributes are used to classify this wine? Given Chlorides of 0. What percentage of the wine data is classified correctly by the tree? When applying cross-validation the observed data is split into training and test sets, i. We train the model on the training data and evaluate the performance of the trained model on the test data. Divide the data into a training and a test data set see hints! This will show us what the optimal pruning depth is for the training set, but also how well this generalizes to a test set.

Note that the package also contains functions to partition data for K-fold cross-validation. Some of the functions can ensure that both training and test sets have roughly the same class proportions. The error is 1-accuracy. Do you get the same result when you run your code again, generating a new random split between training and test data? It grows with train accuracy until a certain depth around 5 , then starts deviating down as expected - however, surprisingly, it does not fall as much as could be expected due to overfitting, remaining rather flat for depths greater than 8.

Let's see what happens when we do lots of random splits, and allow more depth! K-fold cross-validation works by splitting X into K partitions of equal length. Then in each fold, you choose K-1 partitions as the training set and the remaining partition as the test set.

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