Misclassification Error Tree
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Decision Tree Error Rate
3 dialog. At Output Variable, select CAT. MEDV, then from the Selected Variables list, select all remaining variables except MEDV. The MEDV variable is not included, since the CAT. MEDV variable is derived from the MEDV variable. At Specify "Success" class (for Lift Chart), choose the value that will be the indicator of Success by clicking the down arrow. In this example, we will use the default of 1. At Specify initial cutoff probability for success, enter a value between 0 and 1. If the Probability of success (probability of the output variable = 1) is less than this value, then a 0 is entered for the class value; otherwise, a 1 is entered for the class value. In this example, we will keep the default of 0.5. For Maximum number of tree, keep the default of 7. Click Next to advance to the Classification Tree - Step 2 of 3 dialog. XLMiner normalizes the data when Normalize Input Data is selected. Normalization helps only if l
Market Basket Analysis Neural Network M/M/s Queuing Spreadsheet Q-Learning Support Vector Machines Software Interactive Programs Micro-PedSim Service Blog Jewels of Morning Dew About Resume Frequently Asked Questions Contact Donate Link to Us Terms of Use How to Measure Impurity? By Kardi Teknomo, PhD. < Previous | Next | Content > Click here to purchase the
In A Decision Tree, If The Misclassification Error Rate Is 0.5, Then The Entropy Of The Split Is
complete E-book of this tutorial Given a data table that contains attributes and class of the attributes, we how to calculate accuracy of decision tree in r can measure homogeneity (or heterogeneity) of the table based on the classes. We say a table is pure or homogenous if it contains only a single misclassification rate interpretation class. If a data table contains several classes, then we say that the table is impure or heterogeneous. There are several indices to measure degree of impurity quantitatively. Most well known indices to measure degree of impurity are entropy, gini index, and classification http://www.solver.com/xlminer/help/classification-tree-example error. The formulas are given below All above formulas contain values of probability of a class j. In our example, the classes of Transportation mode below consist of three groups of Bus, Car and Train. In this case, we have 4 buses, 3 cars and 3 trains (in short we write as 4B, 3C, 3T). The total data is 10 rows. Based on these data, we can compute probability of each class. Since probability is equal to frequency relative, we have Prob (Bus) = 4 http://people.revoledu.com/kardi/tutorial/DecisionTree/how-to-measure-impurity.htm / 10 = 0.4 Prob (Car) = 3 / 10 = 0.3 Prob (Train) = 3 / 10 = 0.3 Observe that when to compute probability, we only focus on the classes , not on the attributes . Having the probability of each class, now we are ready to compute the quantitative indices of impurity degrees. Entropy One way to measure impurity degree is using entropy. Example: Given that Prob (Bus) = 0.4, Prob (Car) = 0.3 and Prob (Train) = 0.3, we can now compute entropy as Entropy = - 0.4 log (0.4) ? 0.3 log (0.3) ? 0.3 log (0.3) = 1.571 The logarithm is base 2. Entropy of a pure table (consist of single class) is zero because the probability is 1 and log (1) = 0. Entropy reaches maximum value when all classes in the table have equal probability. Figure below plots the values of maximum entropy for different number of classes n, where probability is equal to p=1/n. I this case, maximum entropy is equal to -n*p*log p. Notice that the value of entropy is larger than 1 if the number of classes is more than 2. Gini Index Another way to measure impurity degree is using Gini index. Example: Given that Prob (Bus) = 0.4, Prob (Car) = 0.3 and Prob (Train) = 0.3, we can now compute Gini index as Gini Index = 1 ? (0.4^2 + 0.3^2 + 0.3^2) = 0.660 Gini index of a pure table (consist of single class) is zero because the probability is 1 and
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