# Decision Trees. The predictor vector x can be a mixture of continuous and categorical variables.

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1 Decision Trees 1 Introduction to tree based methods 1.1 The general setting Supervised learning problem. Data: {(y i, x i ) : i = 1,..., n}, where y is the response, x is a p-dimensional input vector, and n is the sample size. The response y can be binary or categorical classification (or decision) trees; or continuous regression trees. The predictor vector x can be a mixture of continuous and categorical variables. A tree-based method (or recursive partitioning) recursively partitions the predictor space to model the relationship between the response y and the predictors (x 1,..., x p ). 1.2 Advantages of trees It is nonparametric requiring few statistical assumptions. It can be applied to various data structures including both ordered and categorical variables in a simple and natural way. In particular, the recursive partitioning method is exceptionally efficient in handling categorical predictors. It ameliorates the curse of dimensionality. When p > 2, the conventional nonparametric smoothing techniques become computationally infeasible. As p increases, parametric models encounter problems too, such as variable selection, transformations, and interaction handling. It does stepwise variable selection, complexity reduction, and (implicit) interaction handling in an automatic manner. 16

2 Invariant under all monotone transformations of individual ordered predictors Efficient in handling Missing values and provides variable importance rankings. The output gives easily understood and interpreted information. Interpretability is one of the main advantages of decision trees compared to black box methods such as neural networks. The hierarchical (binary) tree structure automatically and optimally groups data, which renders it an excellent tool in medical prognosis/diagnosis. Provides a natural platform to handle heterogeneity in the data by allowing different models to be fit to different groups (treed models). Tradeoff: tree models are robust yet unstable. 1.3 A brief history of tree modeling Morgan and Sonquist (1963) -Automatic Interaction Detection (AID) Breiman, Friedman, Olshen and Stone (1984) - Classification And Regression Trees (CART) Addressed the tree size selection (pruning) and many other issues such as missing values, variable importance, etc. Greatly advanced the use of tree methods in various application fields. Extensions: Freund and Schapire (1996) and Friedman (2001): Boosting Breiman (1996): Bagging Breiman (2003): Random Forests 1.4 An example and terminology The stage C prostate cancer example The dataset contains info about 146 stage C prostate cancer patients. The main clinical endpoint of interest is whether the disease recurs after initial surgical removal of the prostate, and the time interval 17

3 to that progression (if any). The enpoint of this example is pgstat, which takes on the value 1 if the disease has progressed and 0 if not. Below is a short description of the variables. The data is a matrix of 146 rows and 8 columns corresponding to the following 8 variables: pgtime = time to progression in years pgstat = status at last follow-up: 1=progressed, 0=censored age = age at diagnosis eet = early endocrine therapy: 1=no 2=yes g2 = % of cells in g2 phase, from flow cytometry grade = tumor grade 1,2,3,4 gleason = Gleason score (competing grading system, 3-10) ploidy = diploid/tetraploid/aneuploid DNA pattern The file stagec.r shows how to construct a classification tree predicting pgstat from the last 6 variables (age, eet, g2, grade, gleason, ploidy). Terminology: node, root node, parent node, child node, split, leaf (terminal) node, internal node, and path. Built from root node (top) to leaf/terminal nodes (bottom) A record first enters the root node. A test (split) is applied to determine to which child node it should go next. The process is repeated until a record arrives at a leaf (terminal) node. The path from the root to a leaf node provides an expression of a rule. 1.5 References CART by Breiman, Friedman, Olshen, and Stone (1984). Sections 2.2 and 2.7. An introduction to recursive partitioning using the RPART routines by Atkinson and Therneau, Mayo Foundation, February 11, Statistical learning from a regression perspective by Berk (2008) Sections 18

4 Decision Trees (continued) 2 Growing a Large Tree We will follow the CART methodology to develop tree models, which contains the following three major steps: 1. Grow a large initial tree, T 0 ; 2. Iteratively truncate branches of T 0 to obtain a sequence of optimally pruned (nested) subtrees; 3. Select the best tree size based on validation provided by either test sample or cross validation (CV). To illustrate, we consider decision trees with binary responses. Namely, y i = 1 when an event of interest occurs to subject i; 0 otherwise. In this section, we focus on how to grow a large tree. The four elements needed in the initial tree growing procedure are 1. A set of binary questions to induce a split 2. A goodness of split criterion to evaluate a split 3. A stop-splitting rule 4. A rule for assigning every terminal node to a class (0 or 1) We will discuss each of these elements in the sections that follow. 2.1 Possible Number of Splits The first problem in tree construction is how to determine the number of partitions needed to examine at each node. An exhaustive (greedy) search algorithm considers all possible partitions of all input variables at every node in the tree. However, the number of child nodes tends to increase rapidly when there are too many 19

5 variables or when there are too many levels in one or more variables. This makes an exhaustive search algorithm prohibitively expensively. Examples 1. Suppose x is an ordinal variable with four levels 1, 2, 3, and 4. What is the total number of possible splits considering only binary ones? Solution: 2 way split: 1-234, 12-34, Note that there are L 1 possible splits for an ordinal variable with L levels. 2. Suppose x is a numerical variable with 100 distinct values. What is the total number of possible splits? Solution: The formula above for computing the number of possible partitions for ordinal variables also applies when computing the number of possible partitions for numerical variables, where L now denotes the number of distinct values in the observed sample. Total number of splits = = Suppose x is a nominal variable with four categories a, b, c, d. What is the total number of possible binary splits? Solution: ab-cd, ac-bd, ad-bc, abc-d, abd-c, acd-b, a-bcd Total number of binary splits = 7 Note that the total number of possible binary splits is 2 L 1 1. Reducing the Number of Possible Partitions for Nominal Variables For categorical predictors that has many levels {b 1,..., b L }, one way to reduce the number of splits is to rank the levels as {b l1,..., b ll } according to the occurrence rate within each node p{1 b l1 } p{1 b l2 } p{1 b ll } and then treat it as an ordinal input. (See CART, p. 101). 20

6 2.2 Node Impurity based Splitting criteria In general, the impurity i(t) of node t can be defined as a nonnegative function of p{0 t} and p{1 t}, where p{0 t} and p{1 t} denote the proportions of the cases in node t belonging to classes 0 and 1, respectively. More formally, i(t) = φ(p 1 ), where p 1 = p{y = 1 t} and the impurity function φ( ) is the largest when both classes are equally mixed together and it is the smallest when the node contains only one class. Hence, it has the following properties: 1. φ(p) 0; 2. φ(p) attains its minimum 0 when p = 0 or p = φ(p) attains its maximum when p = 1 p = 1/2. 4. φ(p) = φ(1 p), i.e., φ(p) is symmetric about p = 1/2. Common choices of φ include: the minimum error, the entropy function, and the Gini index. The minimum or Bayes Error φ(p) = min(p, 1 p). This measure corresponds to the misclassification rate when majority vote is used. The minimum error is rarely used in practice due to the fact that it does not sufficiently reward purer nodes (CART, p. 99). The Entropy Function φ(p) = p log(p) (1 p) log(1 p). Quinlan (1993) first proposed to use the reduction of Entropy as a goodness of split criterion. Ripley (1996) showed the entropy reduction criterion is equivalent to using the likelihood ratio chi-square statistic for association between the branches and the target categories. 21

7 The Gini Index φ(p) = p(1 p). Breiman et al. (1984) proposed to use the reduction of Gini index as a goodness of split criterion. It has been observed that this rule has an undesirable endcut preference problem (Breiman et al., 1984, Ch. 11): It gives preference to the splits that result in two child nodes of extremely unbalanced sizes. To resolve this problem, a modification called the delta splitting method has been adopted in both the THAID (Morgan and Messenger, 1973) and CART programs. Because of the above concerns, from now on the impurity refers to the entropy criterion unless stated otherwise. Computation of i(t) The computation of impurity is simple when the occurrence rate p{y = 1 t} in node t is available. In many applications such as prospective studies, this occurrence rate can be estimated empirically from the data. At other times (e.g. retrospective studies), additional prior information may be required to estimate the occurrence rate. For a given split s, we have the following 2 2 table according to the split and the response. response node 0 1 left (t L ) n 11 n 12 n 1 right (t R ) n 21 n 22 n 2 n 1 n 2 n In prospective studies, p = p{y = 1 t L } and 1 p = p{y = 0 t L } can be estimated by n 12 /n 1 and n 11 /n 1, respectively. Hence i(t L ) = n 12 n 1 log ( ) n12 n 1 n 11 n 1 log ( n11 n 1 ). 22

8 In fact, it can be shown that the above entropy criterion is proportional to the maximized log-likelihood associated with t L. In light of this fact, many nodesplitting criteria originate from the maximum of certain likelihood functions. The importance of this observation will be appreciated later. Goodness-of-Split Measure Let s be any candidate split and suppose s divides t into t L and t R such that the proportions of the cases in t go into t L and t R are p L and p R, respectively. Define the reduction in node impurity as i(s, t) = i(t) [p L i(t L ) + p R i(t R )], which provides a goodness-of-split measure for s. provides the maximum impurity reduction, i.e., The best split s for node t i(s, t) = max i(s, t). s S Then t will be split into t L and t R according to the split s and the search procedure for the best split repeated on t L and t R separately. A node becomes a terminal node when prespecified terminal node conditions are satisfied. 2.3 Alternative Splitting Criteria There are two alternative splitting criteria: the twoing rule and the χ 2 test. The twoing rule is an alternative measure of the goodness of a split: p L p R 4 j=0,1 p{y = j t L } p{y = j t R } For a binary response, the twoing rule coincides with the use of the Gini index, which has the end-cut preference problem. The Pearson chi-square test statistic measures the difference between the observed cell frequencies and the expected cell frequencies (under the independence assumption). The p-value associated with the χ 2 test may be used as a goodness of split measure

9 2.4 Input variables with different number of possible splits There are more splits to consider on a variable with more levels. Therefore, the maximum possible value for the goodness of split measure tends to become large as the number of possible splits, m, increases. For example, there is only one split for a binary input variable and there are 511 possible binary splits for a nominal input variable with 10 levels. Thus, all commonly used splitting criteria (e.g. Gini index, Entropy, and Pearson χ 2 test) favor variables with large number of possible splits. This problem has been identified as the variable selection bias problem (Loh 2002). Adjustment for Gini index is unavailable. The information gain ratio can be used to adjust Entropy (Quinlan, 1993). information gain ratio = Entropy input levels in parent node. Bonferroni type of adjustment can be used to adjust the χ 2 test (Kass, 1980). Kass adjustment is to multiply the p-value by m, the number of possible splits. In order to identify the unbiased split, Loh (2002) proposed a residual-based method of selecting the most important variable first, and then applying greedy search only on this variable to find the best cutpoint. 2.5 References [1] Statistical learning from a regression perspective by Berk (2008). Section 3.3. [2] Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984) Classification and Regression Trees, Chapman and Hall. Chapters 2 and 4. [3] Kass, G. V. (1980) An Exploratory Technique for Investigating Large Quantities of Categorical Data,, Applied Statistics, Vol. 29, pp [4] Loh, W.-Y. (2002). Regression trees with unbiased variable selection and interaction detection. Statistica Sinica, 12:

10 3 Tree Pruning 3.1 Motivation Why need tree pruning? Decision Trees (continued) Want a final tree model that can generalize well to new data A decision tree can be grown until every node is pure so that the misclassification rate is 0 A small tree with only few branches may fail to adapt enough signals. A Simulated Example from CART (page 60) # Terminal nodes Estimated Rate True Rate Note that in the above table 25

11 The difference between the estimated misclassification rate and true misclassification rate is getting bigger after a certain number of nodes The optimal number of terminal nodes is 10 in this example. Trees with less than 10 nodes under-fit the data. Trees with more than 10 nodes over-fit the data. Balance Between Bias and Variance Tree complexity can be measured by the number of leaves, the number of splits, or the depth. A well-fitted tree has low bias (i.e., adapts enough signal) and low variance (i.e., does not adapt to noise). The determination of tree complexity usually involves the balance between bias and variance. An under-fitted tree that has no sufficient complexity has high bias and low variance. On the other hand, an over-fitted tree has low bias and high variance. 3.2 Before CART: Top-Down Pruning by Stopping Rules Instead of growing a large tree, one may use a set of stopping rules in order to decide when one can declare a terminal node. The strategies for stopping the growth of a tree may include: (1) limit the depth of the tree, (2) set the minimum number of cases in a terminal node, (3) set the minimum statistical significance that a split has to reach. Problems for Top-Down Pruning Stopping rules are often subjective. Both underfitting and overfitting problems may occur. Treatment: bottom-up pruning procedures 26

12 3.3 Misclassification Cost and Cost-Complexity Measure A few notes: CART: First grow a large initial tree T 0 by using loose stopping rules and then select a subtree of T 0 as the best tree structure. Unfortunately, evaluating all possible subtrees would not be computationally feasible even for moderately sized trees, because the number of subtrees grows much faster than the number of terminal nodes in the initial tree. To narrow down the choices of subtrees from which the best-sized subtree is to be selected, CART employs the idea of iteratively pruning off the weakest link to obtain a nested set of best subtrees of size ranging from 1 to T 0. In CART, the complexity of a tree model is determined by the total number of terminal nodes it has. Some tree terminology: Descendant and Ancestor: A node t is called a descendant of a higher node h if there is a connected path down the tree leading from h to t. If t is a descendant node of h, then h is an ancestor of t. Set and size of terminal nodes: Let T denote the set of all terminal nodes of T and T T the set of all internal nodes of T. Furthermore, let denote cardinality, i.e., the number of elements in a set. Therefore, T represents the number of terminal nodes of tree T. Note that T = 2 T 1. Subtree of T: A tree T 1 is a subtree of T if T 1 has the same root node as T and for every h T 1, h T. We denote subtree by : T 1 T. Branch: A tree T h is called a branch of T if T h is a tree with root node h T and all descendants of h in T are descendants of h in T h. Pruning a Branch: Pruning a Branch T h from a tree T consists of deleting from T all descendants of h, that is, cutting off all of T h except its root node. The pruned tree is denoted by T T h. 27

13 Illustration of concepts: page 31 (Source: LeBlanc and Crowley, JASA, 1993) To evaluate branches, define the goodness-of-fit of a tree as R(T ) = t T R(t) where R(t) measures the quality (or goodness-of-fit) of node t. Because our ultimate goal is to classify objects, R(t) is commonly chosen as the misclassification rate. Two types of errors False Positive Error: a case with true response value 0 (-) is falsely classified as 1 (+). False Negative Error: a case with true response value 1 (+) is falsely classified as 0 (-). Two types of errors may need to be weighted with different costs. Modifying Majority Voting by Incorporating Misclassification Cost The class membership of 0 or 1 for a node now depends on whether the total cost of the false positive errors is higher or lower than that of the false negative errors. Let c(i j) denote the cost associated with misclassifying a true j as i. Node t will be assigned to class j (j = 0, 1) if it has the smallest misclassification cost, i.e., c(j 1 j) p(y = 1 j t) c(1 j j) p(y = j t) or c(j y i ) c(1 j y i ) i : y i t i : y i t y i j y i 1 j 28

14 Example: consider a node with 44 preterm (1) and 356 full term (0) babies. Using a simple majority vote principle, the node will be classified as full term. However, in order to minimize the error of misclassifying preterm babies as term babies, we may define the costs to be c(1 0) = 1 and c(0 1) = 10. What class will the node be assigned to? The goodness-of-fit measure R(T ) alone is not sufficient for determining which subtree is better especially because larger trees typically have smaller values of R(T ). To develop a better measure of the performance (predictive ability) of tree T, we need to penalize the misclassification cost by its size, i.e., T. Define the Cost-Complexity Measure of Tree T as R α (T ) = R(T ) + α T where α 0 is the complexity parameter, used to penalize large trees. 3.4 CART: Cost-Complexity Pruning If the complexity parameter α is 0, then the initial tree T 0 is the best, i.e., having the smallest cost-complexity measure; if the complexity parameter goes to infinity, then the tree containing the root node only is the best. Note that as the complexity parameter increases from 0, there will be a link or internal node h that first becomes ineffective. What do we mean by ineffective? The node h as a terminal node is better than the branch T h, i.e., R α (h) R α (T h ) or R(h) + α 1 R(T h ) + α T h or α R(h) R(T h) T h 1 Let α = R(h) R(T h) T, which is the threshold that changes an internal node (link) h 1 h to a terminal node. Compute such threshold for every link (internal node). The link corresponding to the smallest threshold is identified as the weakest link h. 29

15 Denote the pruned subtree, after truncating T h, as T 1 = T 0 T h and repeat the same procedure by considering all internals nodes of T 1 and pruning off the weakest link to obtain T 2. The Algorithm: Let j = 0 Let T = T 0 While T 2, do For every h T T, compute α h = R(h) R(T h) T h 1 Set j = j + 1 enddo Let α j = min α h and h be the corresponding link Let T j = T T h Set T = T j The pruning algorithm results in a nested sequence of optimally pruned subtrees T 0 T 1 T m, where T m denotes the tree with the root node only, and a corresponding sequence of thresholds satisfying 0 = α 0 < α 1 < < α m. CART shows that for α [α k, α k+1 ), k = 0,..., m, tree T k is the smallest subtree that minimizes the cost-complexity measure R α (T ). 3.5 References [1] CART. Sections (pp ). 30

16 Insert: LeBlanc s 1993 JASA paper, page

17 4 Tree Size Selection Decision Trees (continued) As the third step in the CART algorithm, now we need to identify one (or several) optimally-sized tree from the subtree sequence as the final tree model. This step is equivalent to selecting the best tree size. A natural approach is to choose the subtree that optimizes an estimate of a performance measure. However, the resubstitution estimate based on training sample tends to be over optimistic because of the very adaptive nature of decision trees. Need validation methods (test sample & cross validation) to develop a more honest estimate of performance. 4.1 The Test Sample Method Step 1: Split data randomly into two sets: the learning sample L 1 (66.66%) and the test sample L 2 (33.33%) The learning sample is also called the training sample and the test sample is sometimes called the validation sample. The above ratio (2:1) is quoted from CART. A different ratio may be applied depending on the total sample size. For example, when a huge amount of data is available, one may apply a larger proportion for test sample, e.g. a ratio of 1:1 for the learning and test samples. Stratified sampling may be applied. Stratification may be based on the outcome variable or important input variables. Step 2: Using the training sample L 1 only, grow a large initial tree and then prune it back to obtain a nested sequence of subtrees T 0... T M. Step 3: Send the test sample L 2 down each subtree and compute the misclassification cost R ts (T m ) based on the test sample for each subtree T m, m = 0, 1,..., M. The subtree having the smallest misclassification cost is then selected as the best subtree. We denote it as T. That is, R ts (T ) = min m Rts (T m ) 32

18 . Once the best subtree T is determined, R ts (T ) is used as an estimate of the misclassification cost. Advantages and disadvantages of the test sample method Very straight forward. Does not use all data: the sequence of subtrees, from which the best tree model is selected, is based on 2/3 of the data. The estimate of the misclassification cost R(T ) is based on 1/3 of the data, hence the test sample based estimator of the tree performance has high variance when we don t have much data. 4.2 Cross Validation (CV) Often applied when the sample size is moderate or small. (Even when the sample size seems to be large, we may still not have data to waste if the target variable is sparse or if there are a large number of input variables.) Does not waste data. One of the resampling techniques: generate samples from the one sample at hand. Other resampling techniques include bootstrap and jackknife. V -fold cross-validation: Step 1: The whole sample L is randomly divided into V subsets: L v, v = 1,..., V. The sample sizes of the V subsets should be all equal, or as nearly equal as possible. The vth learning sample is L (v) = L L v, v = 1,..., V. The vth subset L v is used as the test sample corresponding to L (v). The value of V needs to be reasonably large so that the size of each training sample, (V 1)/V, is close to the size of all data. CART s suggestion is V = 10, in which case, each training sample contains 90% of the data and each test sample contains 10% of the data. 33

19 Stratified sampling may be used to ensure balance for important variables. Step 2: For a fixed v = 1,..., V, grow a large initial tree and prune it back using only L (v). The pruning procedure provides a nested sequence of optimally pruned subtrees T (v) 0 T (v) 1... T (v) M. Also grow and prune a tree based on all data to obtain the nested sequence of best pruned subtrees T 0 T 1... T M and a corresponding sequence of complexity parameters 0 = α 0 < α 1 <... < α M < α M+1 =. step 3: Now we want to select the best subtree from the subtree sequence T 0 T 1... T M based on the minimum misclassification cost. How do we achieve this through V-fold cross validation? Let s first review an important property from the CART pruning procedure: Theorem (Theorem 3.10 in CART, page 71): For m 1, T m is the smallest subtree that minimizes the cost-complexity measure R α (T ) for complexity parameter α such that α m α < α m+1. The above theorem implies that we can get the optimally pruned subtree for any penalty α from the efficient pruning algorithm. Define α m = α m α m+1 : m = 0, 1,..., M such that for each m, α m is the geometric midpoint of the interval [α m, α m+1 ). Here, {α m : m = 0, 1,..., M + 1} are obtained by applying the cost-complexity pruning algorithm to the entire sample L. Note that for each v = 1,..., V, we have the optimally pruned subtree T (v) (α m) for complexity parameter α m, m = 1,..., M. Now we want to find the complexity parameter α that minimizes the average of the estimated misclassification cost for v = 1,..., V. Fix the value of v : For each m = 1,..., M, L v is sent down the tree T (v) (α m). The quantity R CV v (T (v) (α m)) = t T (v) (α m) i: (x i,y i ) t L v R(i) is calculated, where R(i) is the misclassification cost for observation i that belongs to L v and falls into terminal node t. 34

20 Sum over v: the above quantity is summed over the V subsamples to obtain R CV (T (α m)) = V v=1 R CV v (T (v) (α m)). The best pruned subtree can be defined as the subtree T (α ) which minimizes the cross-validated estimate of the misclassification cost: SE rule R CV (T (α )) = min m RCV (T (α m)). There is one problem with methods based on honest estimates of the misclassification cost (test sample & cross-validation). The estimate of the misclassification cost (or prediction error) tends to decrease rapidly as the tree size increases from the root node. Then there is a wide flat valley with the estimated misclassification cost rising slowly as the number of terminal nodes gets large (see figure on page 79 of CART). Breiman et al. (1984) note that there may be considerable variability in the minimum misclassification cost. CART proposes an ad hoc fix, namely the 1-SE rule. The 1-SE Rule is designed To keep the tree as simple as possible without sacrificing much accuracy To reduce instability in tree selection. CART selects the smallest subtree T such that ˆR(T ) is less than one standard error greater than ˆR(T ), where ˆR denotes either R ts or R cv, and T denotes the best subtree from the corresponding validation method. Namely, for all subtrees with ˆR less than ˆR(T ) + SE( ˆR(T )), T has the smallest size. 4.4 References [1] Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984) Classification and Regression Trees, Chapman and Hall. Sections

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