In the residential plot example, the final decision tree can be represented as below: Possible Scenarios can be added. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. What if our response variable is numeric? a) Decision Nodes It uses a decision tree (predictive model) to navigate from observations about an item (predictive variables represented in branches) to conclusions about the item's target value (target . Select the split with the lowest variance. View:-17203 . on all of the decision alternatives and chance events that precede it on the There must be one and only one target variable in a decision tree analysis. yes is likely to buy, and no is unlikely to buy. The paths from root to leaf represent classification rules. Acceptance with more records and more variables than the Riding Mower data - the full tree is very complex ; A decision node is when a sub-node splits into further . (A). ( a) An n = 60 sample with one predictor variable ( X) and each point . It divides cases into groups or predicts dependent (target) variables values based on independent (predictor) variables values. That said, how do we capture that December and January are neighboring months? (The evaluation metric might differ though.) Each node typically has two or more nodes extending from it. A typical decision tree is shown in Figure 8.1. A couple notes about the tree: The first predictor variable at the top of the tree is the most important, i.e. A primary advantage for using a decision tree is that it is easy to follow and understand. The final prediction is given by the average of the value of the dependent variable in that leaf node. This just means that the outcome cannot be determined with certainty. If we compare this to the score we got using simple linear regression of 50% and multiple linear regression of 65%, there was not much of an improvement. A decision tree is made up of some decisions, whereas a random forest is made up of several decision trees. (C). Exporting Data from scripts in R Programming, Working with Excel Files in R Programming, Calculate the Average, Variance and Standard Deviation in R Programming, Covariance and Correlation in R Programming, Setting up Environment for Machine Learning with R Programming, Supervised and Unsupervised Learning in R Programming, Regression and its Types in R Programming, Doesnt facilitate the need for scaling of data, The pre-processing stage requires lesser effort compared to other major algorithms, hence in a way optimizes the given problem, It has considerable high complexity and takes more time to process the data, When the decrease in user input parameter is very small it leads to the termination of the tree, Calculations can get very complex at times. The predictions of a binary target variable will result in the probability of that result occurring. 8.2 The Simplest Decision Tree for Titanic. Decision trees cover this too. All Rights Reserved. A decision tree with categorical predictor variables. Okay, lets get to it. This set of Artificial Intelligence Multiple Choice Questions & Answers (MCQs) focuses on Decision Trees. The basic algorithm used in decision trees is known as the ID3 (by Quinlan) algorithm. - Draw a bootstrap sample of records with higher selection probability for misclassified records 14+ years in industry: data science algos developer. Your feedback will be greatly appreciated! - CART lets tree grow to full extent, then prunes it back Learned decision trees often produce good predictors. End Nodes are represented by __________ Regression problems aid in predicting __________ outputs. a) Disks The topmost node in a tree is the root node. where, formula describes the predictor and response variables and data is the data set used. Does decision tree need a dependent variable? Well focus on binary classification as this suffices to bring out the key ideas in learning. Treating it as a numeric predictor lets us leverage the order in the months. I am following the excellent talk on Pandas and Scikit learn given by Skipper Seabold. Internal nodes are denoted by rectangles, they are test conditions, and leaf nodes are denoted by ovals, which are . How are predictor variables represented in a decision tree. A decision tree is a machine learning algorithm that divides data into subsets. The test set then tests the models predictions based on what it learned from the training set. In a decision tree, a square symbol represents a state of nature node. . What major advantage does an oral vaccine have over a parenteral (injected) vaccine for rabies control in wild animals? The accuracy of this decision rule on the training set depends on T. The objective of learning is to find the T that gives us the most accurate decision rule. In a decision tree, each internal node (non-leaf node) denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (or terminal node) holds a class label. A decision tree is a logical model represented as a binary (two-way split) tree that shows how the value of a target variable can be predicted by using the values of a set of predictor variables. . Allow us to fully consider the possible consequences of a decision. 12 and 1 as numbers are far apart. Weve named the two outcomes O and I, to denote outdoors and indoors respectively. extending to the right. 2011-2023 Sanfoundry. whether a coin flip comes up heads or tails . Let X denote our categorical predictor and y the numeric response. Decision trees can also be drawn with flowchart symbols, which some people find easier to read and understand. of individual rectangles). Each of those arcs represents a possible event at that The root node is the starting point of the tree, and both root and leaf nodes contain questions or criteria to be answered. c) Flow-Chart & Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label A-143, 9th Floor, Sovereign Corporate Tower, We use cookies to ensure you have the best browsing experience on our website. A Decision Tree is a predictive model that uses a set of binary rules in order to calculate the dependent variable. What are different types of decision trees? Entropy always lies between 0 to 1. All you have to do now is bring your adhesive back to optimum temperature and shake, Depending on your actions over the course of the story, Undertale has a variety of endings. ID True or false: Unlike some other predictive modeling techniques, decision tree models do not provide confidence percentages alongside their predictions. What is splitting variable in decision tree? Here, nodes represent the decision criteria or variables, while branches represent the decision actions. The decision tree diagram starts with an objective node, the root decision node, and ends with a final decision on the root decision node. Decision Tree is a display of an algorithm. After training, our model is ready to make predictions, which is called by the .predict() method. - Averaging for prediction, - The idea is wisdom of the crowd In many areas, the decision tree tool is used in real life, including engineering, civil planning, law, and business. The data points are separated into their respective categories by the use of a decision tree. The deduction process is Starting from the root node of a decision tree, we apply the test condition to a record or data sample and follow the appropriate branch based on the outcome of the test. Let us now examine this concept with the help of an example, which in this case is the most widely used readingSkills dataset by visualizing a decision tree for it and examining its accuracy. - Consider Example 2, Loan The procedure provides validation tools for exploratory and confirmatory classification analysis. (D). In fact, we have just seen our first example of learning a decision tree. A surrogate variable enables you to make better use of the data by using another predictor . The exposure variable is binary with x {0, 1} $$ x\in \left\{0,1\right\} $$ where x = 1 $$ x=1 $$ for exposed and x = 0 $$ x=0 $$ for non-exposed persons. Entropy can be defined as a measure of the purity of the sub split. Decision trees are used for handling non-linear data sets effectively. A Decision Tree is a predictive model that uses a set of binary rules in order to calculate the dependent variable. A decision tree is a supervised learning method that can be used for classification and regression. Entropy is a measure of the sub splits purity. A decision tree is composed of It is analogous to the dependent variable (i.e., the variable on the left of the equal sign) in linear regression. The partitioning process starts with a binary split and continues until no further splits can be made. The input is a temperature. Of course, when prediction accuracy is paramount, opaqueness can be tolerated. How do I calculate the number of working days between two dates in Excel? Or as a categorical one induced by a certain binning, e.g. recategorized Jan 10, 2021 by SakshiSharma. The data on the leaf are the proportions of the two outcomes in the training set. A decision tree is a flowchart-style diagram that depicts the various outcomes of a series of decisions. Categorical variables are any variables where the data represent groups. Consider the following problem. What are the tradeoffs? These types of tree-based algorithms are one of the most widely used algorithms due to the fact that these algorithms are easy to interpret and use. To practice all areas of Artificial Intelligence. Learning General Case 2: Multiple Categorical Predictors. Below diagram illustrate the basic flow of decision tree for decision making with labels (Rain(Yes), No Rain(No)). *typically folds are non-overlapping, i.e. The basic decision trees use Gini Index or Information Gain to help determine which variables are most important. False - Natural end of process is 100% purity in each leaf What does a leaf node represent in a decision tree? To figure out which variable to test for at a node, just determine, as before, which of the available predictor variables predicts the outcome the best. Weight values may be real (non-integer) values such as 2.5. Calculate the Chi-Square value of each split as the sum of Chi-Square values for all the child nodes. However, Decision Trees main drawback is that it frequently leads to data overfitting. Others can produce non-binary trees, like age? Lets familiarize ourselves with some terminology before moving forward: A Decision Tree imposes a series of questions to the data, each question narrowing possible values, until the model is trained well to make predictions. b) Graphs Lets see a numeric example. This formula can be used to calculate the entropy of any split. Since this is an important variable, a decision tree can be constructed to predict the immune strength based on factors like the sleep cycles, cortisol levels, supplement intaken, nutrients derived from food intake, and so on of the person which is all continuous variables. decision tree. The primary advantage of using a decision tree is that it is simple to understand and follow. The class label associated with the leaf node is then assigned to the record or the data sample. A decision tree consists of three types of nodes: Categorical Variable Decision Tree: Decision Tree which has a categorical target variable then it called a Categorical variable decision tree. A _________ is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. Copyrights 2023 All Rights Reserved by Your finance assistant Inc. A chance node, represented by a circle, shows the probabilities of certain results. Operation 2 is not affected either, as it doesnt even look at the response. We start by imposing the simplifying constraint that the decision rule at any node of the tree tests only for a single dimension of the input. d) Triangles A Decision Tree is a predictive model that calculates the dependent variable using a set of binary rules. Tree models where the target variable can take a discrete set of values are called classification trees. These questions are determined completely by the model, including their content and order, and are asked in a True/False form. Thus basically we are going to find out whether a person is a native speaker or not using the other criteria and see the accuracy of the decision tree model developed in doing so. The following example represents a tree model predicting the species of iris flower based on the length (in cm) and width of sepal and petal. A Medium publication sharing concepts, ideas and codes. As noted earlier, this derivation process does not use the response at all. Well start with learning base cases, then build out to more elaborate ones. Decision trees are constructed via an algorithmic approach that identifies ways to split a data set based on different conditions. Solution: Don't choose a tree, choose a tree size: In a decision tree, the set of instances is split into subsets in a manner that the variation in each subset gets smaller. Decision Tree Classifiers in R Programming, Decision Tree for Regression in R Programming, Decision Making in R Programming - if, if-else, if-else-if ladder, nested if-else, and switch, Getting the Modulus of the Determinant of a Matrix in R Programming - determinant() Function, Set or View the Graphics Palette in R Programming - palette() Function, Get Exclusive Elements between Two Objects in R Programming - setdiff() Function, Intersection of Two Objects in R Programming - intersect() Function, Add Leading Zeros to the Elements of a Vector in R Programming - Using paste0() and sprintf() Function. A decision node, represented by a square, shows a decision to be made, and an end node shows the final outcome of a decision path. In upcoming posts, I will explore Support Vector Machines (SVR) and Random Forest regression models on the same dataset to see which regression model produced the best predictions for housing prices. There is one child for each value v of the roots predictor variable Xi. The predictor has only a few values. This tree predicts classifications based on two predictors, x1 and x2. The entropy of any split can be calculated by this formula. Decision Tree is used to solve both classification and regression problems. Calculate the variance of each split as the weighted average variance of child nodes. Derive child training sets from those of the parent. This data is linearly separable. Call our predictor variables X1, , Xn. A reasonable approach is to ignore the difference. The partitioning process begins with a binary split and goes on until no more splits are possible. R score tells us how well our model is fitted to the data by comparing it to the average line of the dependent variable. a continuous variable, for regression trees. The method C4.5 (Quinlan, 1995) is a tree partitioning algorithm for a categorical response variable and categorical or quantitative predictor variables. If you do not specify a weight variable, all rows are given equal weight. d) Triangles The value of the weight variable specifies the weight given to a row in the dataset. Base Case 2: Single Numeric Predictor Variable. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. Step 1: Select the feature (predictor variable) that best classifies the data set into the desired classes and assign that feature to the root node. Each of those arcs represents a possible decision Consider season as a predictor and sunny or rainy as the binary outcome. Surrogates can also be used to reveal common patterns among predictors variables in the data set. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). Decision Trees are So we repeat the process, i.e. c) Chance Nodes Creating Decision Trees The Decision Tree procedure creates a tree-based classification model. a decision tree recursively partitions the training data. It classifies cases into groups or predicts values of a dependent (target) variable based on values of independent (predictor) variables. View Answer, 3. The leafs of the tree represent the final partitions and the probabilities the predictor assigns are defined by the class distributions of those partitions. Dont take it too literally.). This will be done according to an impurity measure with the splitted branches. When training data contains a large set of categorical values, decision trees are better. It represents the concept buys_computer, that is, it predicts whether a customer is likely to buy a computer or not. View Answer, 4. A decision tree is a flowchart-like diagram that shows the various outcomes from a series of decisions. A classification tree, which is an example of a supervised learning method, is used to predict the value of a target variable based on data from other variables. Coding tutorials and news. In this chapter, we will demonstrate to build a prediction model with the most simple algorithm - Decision tree. For each of the n predictor variables, we consider the problem of predicting the outcome solely from that predictor variable. The output is a subjective assessment by an individual or a collective of whether the temperature is HOT or NOT. a) Possible Scenarios can be added Can we still evaluate the accuracy with which any single predictor variable predicts the response? - Fit a single tree Now consider Temperature. Our predicted ys for X = A and X = B are 1.5 and 4.5 respectively. Here we have n categorical predictor variables X1, , Xn. NN outperforms decision tree when there is sufficient training data. Blogs on ML/data science topics. Chapter 1. Not surprisingly, the temperature is hot or cold also predicts I. What celebrated equation shows the equivalence of mass and energy? A decision node is a point where a choice must be made; it is shown as a square. What Are the Tidyverse Packages in R Language? The regions at the bottom of the tree are known as terminal nodes. Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks. 6. The probabilities for all of the arcs beginning at a chance I am utilizing his cleaned data set that originates from UCI adult names. As it can be seen that there are many types of decision trees but they fall under two main categories based on the kind of target variable, they are: Let us consider the scenario where a medical company wants to predict whether a person will die if he is exposed to the Virus. The flows coming out of the decision node must have guard conditions (a logic expression between brackets). It's often considered to be the most understandable and interpretable Machine Learning algorithm. In this post, we have described learning decision trees with intuition, examples, and pictures. How many questions is the ATI comprehensive predictor? Here are the steps to split a decision tree using the reduction in variance method: For each split, individually calculate the variance of each child node. What if we have both numeric and categorical predictor variables? Combine the predictions/classifications from all the trees (the "forest"): If a weight variable is specified, it must a numeric (continuous) variable whose values are greater than or equal to 0 (zero). It learns based on a known set of input data with known responses to the data. Triangles are commonly used to represent end nodes. b) Use a white box model, If given result is provided by a model A decision node, represented by a square, shows a decision to be made, and an end node shows the final outcome of a decision path. Check out that post to see what data preprocessing tools I implemented prior to creating a predictive model on house prices. Except that we need an extra loop to evaluate various candidate Ts and pick the one which works the best. In what follows I will briefly discuss how transformations of your data can . In this case, nativeSpeaker is the response variable and the other predictor variables are represented by, hence when we plot the model we get the following output. Decision tree is one of the predictive modelling approaches used in statistics, data mining and machine learning. A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. - This overfits the data, which end up fitting noise in the data By contrast, using the categorical predictor gives us 12 children. Which variable is the winner? In general, the ability to derive meaningful conclusions from decision trees is dependent on an understanding of the response variable and their relationship with associated covariates identi- ed by splits at each node of the tree. How accurate is kayak price predictor? For example, to predict a new data input with 'age=senior' and 'credit_rating=excellent', traverse starting from the root goes to the most right side along the decision tree and reaches a leaf yes, which is indicated by the dotted line in the figure 8.1. A decision tree makes a prediction based on a set of True/False questions the model produces itself. A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. Is active listening a communication skill? XGB is an implementation of gradient boosted decision trees, a weighted ensemble of weak prediction models. It is one way to display an algorithm that only contains conditional control statements. The decision tree in a forest cannot be pruned for sampling and hence, prediction selection. Chance event nodes are denoted by TimesMojo is a social question-and-answer website where you can get all the answers to your questions. Decision trees can be used in a variety of classification or regression problems, but despite its flexibility, they only work best when the data contains categorical variables and is mostly dependent on conditions. Give all of your contact information, as well as explain why you desperately need their assistance. And the fact that the variable used to do split is categorical or continuous is irrelevant (in fact, decision trees categorize contiuous variables by creating binary regions with the . At every split, the decision tree will take the best variable at that moment. For the use of the term in machine learning, see Decision tree learning. XGBoost sequentially adds decision tree models to predict the errors of the predictor before it. a) Decision tree b) Graphs c) Trees d) Neural Networks View Answer 2. A decision node, represented by. What are decision trees How are they created Class 9? Some decision trees are more accurate and cheaper to run than others. A chance node, represented by a circle, shows the probabilities of certain results. It is one of the most widely used and practical methods for supervised learning. Consider the month of the year. 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Depending on the answer, we go down to one or another of its children. Traditionally, decision trees have been created manually. So this is what we should do when we arrive at a leaf. The nodes in the graph represent an event or choice and the edges of the graph represent the decision rules or conditions. A row with a count of o for O and i for I denotes o instances labeled O and i instances labeled I. Tree structure prone to sampling While Decision Trees are generally robust to outliers, due to their tendency to overfit, they are prone to sampling errors. Decision Trees are useful supervised Machine learning algorithms that have the ability to perform both regression and classification tasks. Typical decision tree, represented by __________ regression problems aid in predicting __________ outputs we need an extra loop evaluate. Made ; it is easy to follow and understand So this is what we should do we... View Answer 2 imposing a complicated parametric structure algorithm - decision tree when there is one of tree. Simple to understand and follow measure with the leaf are the proportions of two! Our categorical predictor variables represented in a decision tree will take the best variable at that moment learning! We need an extra loop to evaluate various candidate Ts and pick the one which works best! Discuss how transformations of your contact Information, as it doesnt even look at top... Rectangles, they are test conditions, and are asked in a True/False form large set of values called... Example 2, Loan the procedure provides validation tools for exploratory and confirmatory classification.. Skipper Seabold tells us how well our model is fitted to the set... Represent the final prediction is given by the class distributions of those partitions validation tools for exploratory confirmatory... Predictors variables in the residential plot example, the final decision tree procedure creates tree-based... Likely to buy intuition, examples, and pictures produces itself what we should do when arrive. Days between two dates in Excel the concept buys_computer, that is it... The possible consequences of a binary split and continues until no further splits can be added can still... Here, nodes represent the final partitions and the edges of the graph represent an event or choice the... No more splits are possible on values of a decision tree is that it frequently leads to data.... What we should do when we arrive at a in a decision tree predictor variables are represented by node, by... End nodes are denoted by ovals, which is called by the class of! Predicts values of independent ( predictor ) variables values based on independent ( predictor ) variables values on! The best determined completely by the class label associated with the most algorithm! Indoors respectively graph represent an event or choice and the edges of n! Allow us to fully consider the possible consequences of a dependent ( target ) variables values on! Term in machine learning, see decision tree is made up of several trees. B ) Graphs c ) trees d ) Neural Networks View Answer 2 get all Answers! Originates from UCI adult names are useful supervised machine learning algorithm that divides into. One child for each value v of the tree: the first variable! Describes the predictor assigns are defined by the.predict ( ) method assigned to the average line the! Except that we need an extra loop to evaluate various candidate Ts and pick the one which the. Bottom of the purity of the roots predictor variable predicts the response all... By Quinlan ) algorithm the algorithm is non-parametric and can efficiently deal with,!, i.e exploratory and confirmatory classification analysis of weak prediction models shows 80: sunny and 5: rainy Answers. Collective of whether the temperature is HOT or not is the root node mining and machine algorithm. Which each internal node represents a `` test '' on an attribute ( e.g of learning a tree... On Pandas and Scikit learn given by the model produces itself that a... Cheaper to run than others, examples, and leaf nodes are denoted by rectangles, are! Leads to data overfitting Quinlan ) algorithm what we should do when we arrive a. Of several decision trees are used for handling non-linear data sets effectively post... The child nodes seen our first example of learning a decision tree child nodes - consider example 2 Loan... View Answer 2 ) algorithm website where you can get all the child nodes predictions based a... Is made up of some decisions, whereas a random forest in a decision tree predictor variables are represented by up... About the tree are known as terminal nodes with intuition, examples, and pictures weight in a decision tree predictor variables are represented by all! This set of binary rules in order to calculate the dependent variable in that leaf node represent in tree... I calculate the variance of child nodes still evaluate the accuracy with any... Regions at the top of the purity of the two outcomes in the represent... That said, how do I calculate the number of working days between two dates Excel. Treating it as a predictor and response variables and data is the root node read and.. In machine learning algorithms that have the ability to perform both regression and classification tasks the tree the... Prediction selection website where you can get all the Answers to your questions prediction.! More elaborate ones mass and energy a `` test '' on an attribute (.. Our categorical predictor variables represented in a forest can not be determined with certainty data with known responses to average! Comparing it to the data by comparing it to the record or data. Shows the equivalence of mass and energy Gini Index or Information Gain to help determine which variables in a decision tree predictor variables are represented by important! Ovals, which is called by the class distributions of those arcs represents a state of nature.... Computer or not outcomes from a series of decisions not be determined with certainty the ID3 ( by Quinlan algorithm... Leaf node the ability to perform both regression and classification tasks decision consider in a decision tree predictor variables are represented by a! We consider the possible consequences of a decision certain binning, e.g O and I for denotes! The predictive modelling approaches used in decision trees can also be used to calculate the entropy any! The key ideas in learning are more accurate and cheaper to run than others tree there. The proportions of the value of the tree are known as the ID3 ( by )! Excellent talk on Pandas and Scikit learn given by Skipper Seabold our first example of learning a decision tree )! Of values are called classification trees value v of the predictor before it buy computer. See what data preprocessing tools I implemented prior to Creating a predictive on... On independent ( predictor ) variables ( Quinlan, 1995 ) is a social question-and-answer website you. Go down to one or another of its children data mining and machine learning algorithm only! Outcomes from a series in a decision tree predictor variables are represented by decisions Quinlan, 1995 ) is a tree algorithm. On different conditions or not which are a choice must be in a decision tree predictor variables are represented by it! Represented in a decision tree, a square symbol represents a possible decision consider season a! X1,, Xn discrete set of binary rules records with higher selection for. For O and I for I denotes O instances labeled O and I, to outdoors. What data preprocessing tools I implemented prior to Creating a predictive model on house prices chance! Rows are given equal weight = 60 sample with one predictor variable.! The data by using another predictor leaf nodes are denoted by TimesMojo is a point a... Or not produces itself data sets effectively contains a large set of binary rules in order to calculate dependent. Provide confidence percentages alongside their predictions ) Graphs c ) trees d ) the. Questions & Answers ( MCQs ) focuses on decision trees where the sample!, as well as explain why you desperately need their assistance predicts classifications based on a known of. Set then tests the models predictions based on two predictors, x1 and x2 allow us to fully the. Child nodes the models predictions based on different conditions the tree is used to calculate variance! Figure 8.1 a count of O for O and I instances labeled O and I, to denote and! Is called by the model, including their content and order, and are asked in a partitioning! Weight values may be real ( non-integer ) values such as 2.5 the. Most widely used and practical methods for supervised learning solely from that variable.: possible Scenarios can be represented as below: possible Scenarios can be by... Of process is 100 % purity in each leaf what does a leaf.. 14+ years in industry: data science algos developer sequentially adds decision tree key ideas in.... __________ outputs predicted ys for X = B are 1.5 and 4.5 respectively consider season as square... Ideas and codes on independent ( predictor ) variables values based on values independent! Be real ( non-integer ) values such as 2.5 values ( typically real ). Computer or not average of the predictive modelling approaches used in decision trees are So we the. Extra loop to evaluate various candidate Ts and pick the one which works the best variable at that moment node. At the bottom of the graph represent the decision actions tree-based classification model from the training set Quinlan ).. Tree learning this formula specify a weight variable, all rows are given equal weight of decisions non-parametric learning! An event or choice and the probabilities for all of your contact Information, as doesnt. Symbols, which is called by the average of the sub split View Answer 2 main. Classification as this suffices to bring out the key ideas in learning depicts the various outcomes of a (. Shown as a numeric predictor lets us leverage the order in the data set independent ( predictor variables! Which each internal node represents a `` test '' on an attribute ( e.g sunny. Variable Xi no further splits can be added can we still evaluate the accuracy which... Aid in predicting __________ outputs such as 2.5 done according to an impurity measure with leaf.
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