thuộc tính liên tục, trong khi giới tính là một thuộc tính danh nghĩa. Chúng ta biết rằng các tập dữ liệu học tập thực sự là nhỏ, và có khả năng chúng tôi có thể thậm chí không tìm thấy một yếu tố dự báo tốt, nhưng chúng tôi đang quan tâm để cố gắng khai thác các API WEKA, | Using an open source data mining framework WEKA 189 Simpo PDF Merge and Split Unregistered Version - http continuous attribute while gender is a nominal attribute. We know that the learning dataset is really small and potentially we may not even find a good predictor but we re keen to try out the WEKA mining APIs so we go ahead and build the predictive model in preparation for future better times. User Age Gender Number of logins John 20 male 5 Jane 30 female 2 Ed 40 male 3 Table The data associated Amy 35 female 4 with the WEKA API tutorial For our example we do the following five steps 1 Create the attributes. 2 Create the dataset for learning. 3 Build the predictive model. 4 Evaluate the quality of the model built. 5 Predict the number of logins for a new user. We implement a class WEKATutorial which follows these five steps. The code for this class is shown in listing . Listing Implementation of the WEKATutorial package import import import import import import import public class WEKATutorial public static void main String args throws Exception WEKATutorial wekaTut new WEKATutorial Create attributes Build predictive model private void executeWekaTutorial throws Exception FastVector allAttributes createAttributes ---------- Instances learningDataset Create dataset createLearningDataSet allAttributes for learning Classifier predictiveModel learnPredictiveModel learningDataset Evaluation evaluation evaluatePredictiveModel predictiveModel learningDataset Evaluate predictive predictUnknownCases learningDataset predictiveModel - model Predict unknown cases 190 Chapter 7 Data mining process toolkits and standards Simpo PDF Merge and Split Unregistered Version - http .
