Decision Tree is a supervised machine learning algorithm that can be used to solve both classification and regression problems. The goal of this algorithm is to create a model that can predict the value or class of the target variable by learning decision rules inferred from training data.<\/p>\n
When using Decision Trees to predict the class label for a record, we begin from the root of the tree. The values of the root attribute are then compared with the record’s attribute. Based on the comparison, we follow the branch that corresponds to that value and proceed to the next node.<\/p>\n
The Decision Tree algorithm classifies examples by sorting them right from the root node to the leaf\/terminal node, classifying the example.<\/p>\n
In this article, we will be discussing how to implement the Decision Tree algorithm in Java. We will be predicting whether an individual will go to the gym or not based on their age and weight.<\/p>\n
The data has been stored in .csv file named The dataset has two independent variables namely Let’s first import the set of libraries to be used for this:<\/p>\n We can now create a static Java class to represent the GridDB container to be used:<\/p>\n The above Java class is equivalent to a SQL table with 3 columns. The 3 variables represents the columns of the GridDB container.<\/p>\n Let’s now establish a connection between Java and the GridDB container. We will use the credentials of our GridDB installation:<\/p>\n We will be using a GridDB container named We will be using the name Let’s now move data from the The above code will add the data into the GridDB container.<\/p>\n We now want to retrieve the data from the GridDB container and use it to implement a model using the Decision Tree algorithm. The following code can help us to retrieve the data:<\/p>\n The The goal is to use the data to train a machine learning model that can predict whether an individual will go to the gym or not. We will use the Decision Tree algorithm to train the model. Let’s import the necessary libraries from the Weka library:<\/p>\n We can now create a buffered reader and instances for the dataset:<\/p>\n Let us now call the The last instance of the dataset has not been used in building the classifier.<\/p>\n Let us use the above model and the last instance of the dataset to make a prediction. We will use the To compile and run the model, you will need the Weka API. Download it from the following URL:<\/p>\n Next, login as the Run the following command on your Linux terminal to set the path for the gridstore.jar file:<\/p>\n Next, use the following command to compile your Run the .class file that is generated by running the following command:<\/p>\n The model returned Introduction Decision Tree is a supervised machine learning algorithm that can be used to solve both classification and regression problems. The goal of this algorithm is to create a model that can predict the value or class of the target variable by learning decision rules inferred from training data. When using Decision Trees to predict […]<\/p>\n","protected":false},"author":41,"featured_media":28078,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[121],"tags":[],"class_list":["post-46687","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"yoast_head":"\ngym.csv<\/code>, but we need to move it to GridDB. Although we can still use the data from the CSV file, GridDB offers a number of benefits, especially improved query performance.<\/p>\nage<\/code> and weight<\/code> and one dependent variable, gym<\/code>. A value of 1 for the dependent variable shows that a person will go to the gym while a value of 0 shows that the person won’t go to the gym.<\/p>\nimport java.io.File;\nimport java.io.IOException;\nimport java.util.Properties;\nimport java.util.Collection;\nimport java.util.Scanner;\n\n\nimport com.toshiba.mwcloud.gs.Collection;\nimport com.toshiba.mwcloud.gs.GSException;\nimport com.toshiba.mwcloud.gs.GridStore;\nimport com.toshiba.mwcloud.gs.GridStoreFactory;\nimport com.toshiba.mwcloud.gs.Query;\nimport com.toshiba.mwcloud.gs.RowKey;\nimport com.toshiba.mwcloud.gs.RowSet;<\/code><\/pre>\n<\/div>\npublic static class GymData {\n @RowKey int age;\n int weight; \n int gym;\n } <\/code><\/pre>\n<\/div>\n Properties props = new Properties();\n props.setProperty(\"notificationAddress\", \"239.0.0.1\");\n props.setProperty(\"notificationPort\", \"31999\");\n props.setProperty(\"clusterName\", \"defaultCluster\");\n props.setProperty(\"user\", \"admin\");\n props.setProperty(\"password\", \"admin\");\n GridStore store = GridStoreFactory.getInstance().getGridStore(props);<\/code><\/pre>\n<\/div>\nGymData<\/code>. Let’s select it:<\/p>\nCollection<String, GymData> coll = store.putCollection(\"col01\", GymData.class);<\/code><\/pre>\n<\/div>\ncoll<\/code> to refer to the GymData<\/code> container.<\/p>\ngym.csv<\/code> file into GridDB:<\/p>\nFile file1 = new File(\"gym.csv\");\n Scanner sc = new Scanner(file1);\n String data = sc.next();\n \n while (sc.hasNext()){\n String scData = sc.next();\n String dataList[] = scData.split(\",\");\n String age = dataList[0];\n String weight = dataList[1];\n String gym = dataList[2];\n \n \n \n GymData gd = new GymData();\n gd.age = Integer.parseInt(age);\n gd.weight = Integer.parseInt(weight);\n gd.gym = Integer.parseInt(gym);\n \n \n \n coll.append(gd);\n }\n<\/code><\/pre>\n<\/div>\nRetrieve the Data<\/h2>\n
Query<GymData> query = coll.query(\"select *\");\n RowSet<GymData> rs = query.fetch(false);\n RowSet res = query.fetch();<\/code><\/pre>\n<\/div>\nselect *<\/code> means select all<\/code>, and it helps us to retrieve all the data stored in the container.<\/p>\nImplement the Decision Tree Model<\/h2>\n
import java.io.IOException;\n\nimport weka.classifiers.Classifier;\nimport weka.classifiers.Evaluation;\nimport weka.core.Instances;\nimport weka.core.Instance;\nimport weka.core.converters.ArffLoader; \n\nimport java.io.FileReader;\nimport java.io.BufferedReader;\n\nimport java.io.IOException;\nimport weka.classifiers.trees.Id3;\nimport weka.classifiers.trees.J48;\nimport weka.core.converters.ArffLoader;<\/code><\/pre>\n<\/div>\nBufferedReader bufferedReader\n = new BufferedReader(\n new FileReader(res));\n \n \/\/ Create dataset instances\n Instances datasetInstances\n = new Instances(bufferedReader);<\/code><\/pre>\n<\/div>\nbuildClassifier()<\/code> function of the Weka library to build the Decision Tree classifier:<\/p>\ndatasetInstances.setClassIndex(datasetInstances.numAttributes()-1);\n\n \n Classifier myclassifier = new J48();\n \n \n myclassifier.buildClassifier(datasetInstances);\n System.out.println(myclassifier);<\/code><\/pre>\n<\/div>\nMake a Prediction<\/h2>\n
classifyInstance()<\/code> function of the Weka library as shown below:<\/p>\nInstance pred = datasetInstances.lastInstance();\n double answer = myclassifier.classifyInstance(pred);\n System.out.println(answer);<\/code><\/pre>\n<\/div>\nCompile and Run the Model<\/h2>\n
http:\/\/www.java2s.com\/Code\/Jar\/w\/weka.htm<\/code><\/pre>\n<\/div>\ngsadm<\/code> user. Move your .java<\/code> file to the bin<\/code> folder of your GridDB located in the following path:<\/p>\n\/griddb_4.6.0-1_amd64\/usr\/griddb-4.6.0\/bin<\/code><\/pre>\n<\/div>\nexport CLASSPATH=$CLASSPATH:\/home\/osboxes\/Downloads\/griddb_4.6.0-1_amd64\/usr\/griddb-4.6.0\/bin\/gridstore.jar<\/code><\/pre>\n<\/div>\n.java<\/code> file:<\/p>\njavac -cp weka-3-7-0\/weka.jar DecisionTreeAlgorithm.java<\/code><\/pre>\n<\/div>\njava -cp .:weka-3-7-0\/weka.jar DecisionTreeAlgorithm<\/code><\/pre>\n<\/div>\n0<\/code> for the prediction, which means that the person will not go to the gym.<\/p>\n","protected":false},"excerpt":{"rendered":"