![]() You are advised to validate the model on a validation sample wherever possible. Validation for classification and regression trees A model with an AUC greater than 0.9 is excellent. A well-discriminating model must have an AUC of between 0.87 and 0.9. A model is usually considered good when the AUC value is greater than 0.7. For an ideal model, AUC=1 and for a random model, AUC = 0.5. The AUC corresponds to the probability such that a positive event has a higher probability given to it by the model than a negative event. The area under the curve (or AUC) is a synthetic index calculated for ROC curves. It can be used for comparison with other models as it displays the performance of a model. It is the curve of points (1-specificity, sensitivity). When only two classes are present in the dependent variable, the ROC (Receiver Operating Characteristics) curve may also be displayed. If you vary the threshold probability from which an event is to be considered positive, the sensitivity and specificity will also vary. The specificity is the proportion of well-classified negative events. The proportion of well-classified positive events is called the sensitivity. Results for classification and regression trees in XLSTATĪmong the numerous results provided, XLSTAT can display the classification table (also called confusion matrix) used to calculate the percentage of well-classified observations. It allows us to see which portion of the data concentrates the maximum number of positive events. Usually, the higher the Lift, the better the model.Ĭumulative gain curve : The gain curve represents the sensitivity, or recall, as a function of the percentage of the total population. A Lift of 1 means that there is no gain over an algorithm that makes random predictions. Lift is the ratio between the proportion of true positives and the proportion of positive predictions. Lift curve : The Lift curve is the curve that represents the Lift value as a function of the percentage of the population. The terms used come from signal detection theory. The ROC curve ( Receiver Operating Characteristics ) displays the performance of a model and enables a comparison to be made with other models. In the case of a qualitative depending variable with only two categories, the user will be able to compare the performances of both methods by using ROC curves, Lift curves, or Cumulative gain curves. In the case of a Discriminant analysis or logistic regression, only qualitative dependent variables can be used. XLSTAT uses the CHAID, exhaustive CHAID, QUEST and C&RT (Classification and Regression Trees) algorithms.Ĭlassification and regression trees apply to quantitative and qualitative dependent variables. Use regression tree to build an explanatory and predicting model for a dependent quantitative variable based on explanatory quantitative and qualitative variables.Īlgorithms for classification and regression trees in XLSTAT.Use classification trees to explain and predict the belonging of objects (observations, individuals) to a class, on the basis of explanatory quantitative and qualitative variables. ![]() We distinguish the following two cases where these modeling techniques should be used: Two of the strengths of this method are on the one hand the simple graphical representation by trees, and on the other hand the compact format of the natural language rules. What are classification and regression treesĬlassification and regression trees are methods that deliver models that meet both explanatory and predictive goals. ![]()
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