No matter how many steps we look forward, this process will always be greedy. Looking ahead multiple steps will not fundamentally solve this problem. That is if I know a point goes to node t, what is the probability this point is in class j. For a full tree (balanced), the sum of \(N(t)\) over all the node t’s at the same level is N.
Bagging (bootstrap aggregating) was one of the first ensemble algorithms to be documented. The biggest advantage of bagging is the relative ease with which the algorithm can be parallelized, which makes it a better selection for very large data sets. The process starts with a Training Set consisting of pre-classified records (target field or dependent variable with a known class or label such as purchaser or non-purchaser). For simplicity, assume that there are only two target classes, and that each split is a binary partition.
We have noted that in the classification tree, only two variables Start and Age played a role in the build-up of the tree. The creation of the tree can be supplemented using a loss matrix, which defines the cost of misclassification if this varies among classes. For example, in classifying cancer cases it may be more costly to misclassify aggressive tumors as benign than to misclassify slow-growing tumors as aggressive.
Overfitting pruning can be used to prevent the tree from being overfitted just for the training set. This technique makes the tree general for unlabeled data and can tolerate some mistakenly labeled training data. Decision Trees (DTs) are a non-parametric supervised learning method used
for classification and regression. The goal is to create a model that predicts the value of a
target variable by learning simple decision rules inferred from the data
features. Classification Tree Analysis (CTA) is an analytical procedure that takes examples of known classes (i.e., training data) and constructs a decision tree based on measured attributes such as reflectance. In essence, the algorithm iteratively selects the attribute (such as reflectance band) and value that can split a set of samples into two groups, minimizing the variability within each subgroup while maximizing the contrast between the groups.
Pruning a branch \(T_t\) from a tree T consists of deleting from T all descendants of t , that is, cutting off all of \(T_t\) except its root node. We also denote the number of samples going to node t by \(N(t)\), and, the number of samples of class j going to node t by \(N_j(t)\). Let’s begin by introducing the notation N, the total number of samples. The number of samples in class j, \(1 \leq j \leq K\), is \(N_j\) . If we add up all the \(N_j\) data points, we get the total number of data points N. Remember by the nature of the candidate splits, the regions are always split by lines parallel to either coordinate.
This means that while the predictions of a single tree are highly sensitive to noise in its training set, the average of many trees is not, as long as the trees are not correlated. The report also offers the first theoretical result for random forests in the
form of a bound on the generalization error which depends on the strength what is classification tree method of the
trees in the forest and their correlation. Recall that a regression tree maximizes the reduction in the error sum of squares at each split. All of the concerns about overfitting apply, especially given the potential impact that outliers can have on the fitting process when the response variable is quantitative.
Trees are grown to their
maximum size and then a pruning step is usually applied to improve the
ability of the tree to generalize to unseen data. Facilitated by an intuitive graphical display in the interface, the classification rules from the root to a leaf are simple to understand and interpret. Input images can be numerical images, such as reflectance values of remotely sensed data, categorical images, such as a land use layer, or a combination of both. Research seems to suggest that using more flexible questions often does not lead to obviously better classification result, if not worse.
What we would use is the percentage of points in class 1, class 2, class 3, and so on, according to the training data set. For this reason, and because CaRT analysis is relatively new to nursing research, we have sought to temper this discussion with a sample of the validation methodologies described by various healthcare researchers. Validation in CaRT methodology can involve partitioning out and withholding data from larger data sets or testing small subsets of smaller data sets multiple times. Ideally, a CaRT model will be validated on independent data before it can be deemed generalizable. The example provided in Figure Figure22 lacks depth and complexity, yielding less information than may have been uncovered with broadened parameters. The CP eliminates splits that add little or no value to the tree and, in so doing, provides a stopping rule (Lemon et al. 2003).
The partition (splitting) criterion generalizes to multiple classes, and any multi-way partitioning can be achieved through repeated binary splits. To choose the best splitter at a node, the algorithm considers each input field in turn. Every possible split is tried and considered, and the best split is the one that produces the largest decrease in diversity of the classification label within each partition (i.e., the increase in homogeneity).
Overfitting is more likely to occur with more flexible splitting questions. It seems that using the right sized tree is more important than performing good splits at individual nodes. There are classification tree extensions which, instead of thresholding individual variables, perform LDA for every node. Another interesting aspect about the tree in this example is that \(x_6\) and \(x_7\) are never used. This shows that classification trees sometimes achieve dimension reduction as a by-product. Interestingly, in this example, every digit (or every class) occupies exactly one leaf node.