![]() ![]() Going into the analysis of these results is beyond the scope of this tutorial. In the Summary, it says that the correctly classified instances as 2 and the incorrectly classified instances as 3, It also says that the Relative absolute error is 110%. You will very shortly see the visual representation of the tree. Let us examine the output shown on the right hand side of the screen. After a while, the classification results would be presented on your screen as shown here − Selecting ClassifierĬlick on the Choose button and select the following classifier −Ĭlick on the Start button to start the classification process. Now, keep the default play option for the output class − In the percentage split, you will split the data between training and testing using the set split percentage. Under cross-validation, you can set the number of folds in which entire data would be split and used during each iteration of training. Unless you have your own training set or a client supplied test set, you would use cross-validation or percentage split options. You will notice four testing options as listed below − option under the Preprocess tab, click on the Classify tab, and you would see the following screen −īefore you learn about the available classifiers, let us examine the Test options. Open the saved file by using the Open file. ![]() We will use the preprocessed weather data file from the previous lesson. In this chapter, we will learn how to build such a tree classifier on weather data to decide on the playing conditions. So you may prefer to use a tree classifier to make your decision of whether to play or not. Generally, this decision is dependent on several features/conditions of the weather. You may like to decide whether to play an outside game depending on the weather conditions. For example, you may like to classify a tumor as malignant or benign. Many machine learning applications are classification related. ![]()
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