#data data_from_file('data/holiday csv'num_train= target_index=- )print("holiday csv"testdt(dataprint_tree=falsenote that different runs may provide different values as they split the training and test sets differently so if you have hypothesis about what works bettermake sure it is true for different runs exercise the current algorithm does not have very sophisticated stopping criterion what is the current stopping criterion(hintyou need to look at both learn tree and select split exercise extend the current algorithm to include in the stopping criterion (aa minimum child sizedon' use split if one of the children has fewer elements that this (ba depth-bound on the depth of the tree (can improvement bound such that split is only carried out if error with the split is better than the error without the split by at least the improvement bound which values for these parameters make the prediction errors on the test set the smallesttry it on more than one dataset exercise without any input featuresit is often better to include pseudocount that is added to the counts from the training data modify the code so that it includes pseudo-count for the predictions when evaluating splitincluding pseudo counts can make the split worse than no split does pruning with an improvement bound and pseudo-counts make the algorithm work better than with an improvement bound by itselfexercise some people have suggested using information gain (which is equivalent to greedy optimization of log lossas the measure of improvement when building the treeeven in they want to have non-probabilistic predictions in the final tree does this work better than myopically choosing the split that is best for the evaluation criteria we will use to judge the final prediction cross validation and parameter tuning to run the cross validation demoin folder "aipython"load "learncrossvalidation py"using ipython - learncrossvalidation py run the examples at the end to produce graph like figure note that different runs will produce different graphsso your graph will not look like the one in the textbook to try more examplescopy and paste the commented-out commands at the bottom of that file this requires python with matplotlib version june