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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