![]() ![]() ![]() # Use gini as the split criterion tree.imb <- rpart ( cls ~. We will now fit a decision tree by using Gini as the split criterion. The vast majority, 980, of the 1000 observations belong to the “0” class, and only 20 belong to the “1” class. # Load the necessary libraries and the dataset library ( ROSE ) library ( rpart ) library ( ot ) data ( hacide ) # Check imbalance on training set table ( ain $ cls ) # 0 1 # 980 20Īs you may see from the output above, this is a very imbalanced dataset. For building our classification trees, we will use the rpart package. The cls, short for “class”, is the response categorical variable, and \(x_1\) and \(x_2\) are the predictor variables. The dataset has three variables in it for a total of \(N=10^3\) observations. The package ROSE comes with a built-in imbalanced dataset named hacide, consisting of ain and hacide.test. Having said that, there’s a scenario where entropy might be more prudent: imbalanced datasets. So when do we use Gini impurity versus information gain via entropy reduction? Both metrics work more or less the same, and in only a few cases do the results differ considerably. If IG of “Balance” was 0.26 nats and IG of “Education” was 0.14 nats, we would pick the former to split. If we were to choose among “Balance” and some other feature, say “Education”, we would make up our mind based on the IG of both. A node with mixed classes is called impure, and the Gini index is also known as Gini impurity.Ĭoncretely, for a set of items with \(K\) classes, and \(p_k\) being the fraction of items labeled with class \(k\in ) = 0.69 - 0.43 = 0.26nats\] Contrastly, they are maximized if there’s an equal number of observations across all classes in a node. They take small values if most observations fall into the same class in a node. Both of these measures are pretty similar numerically. In classification scenarios that we will be discussing today, the criteria typically used to decide which feature to split on are the Gini index and information entropy. Trees are constructed via recursive binary splitting of the feature space. Today we are going to talk about how the split happens. That’s why bagging, random forests and boosting are used to construct more robust tree-based prediction models. Also, they can be susceptible to changes in the training dataset, where a slight change in it may cause a dramatic change in the final tree. On the downside, trees usually lack the level of predictive accuracy of other methods. Perhaps because they relate to how the human decision-making process works. Decision trees are straightforward to interpret, and as a matter of fact, they can be even easier to interpret than linear or logistic regression models. To make predictions, trees assume either the mean or the most frequent class of the training points inside the region our observation falls, depending on whether we do regression or classification, respectively. They work by segmenting the feature space into several simple subregions. Decision trees are tree-based methods that are used for both regression and classification. ![]()
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