How to Save Your Machine Learning Model and Make Predictions in Weka. After you have found a well performing machine learning model and tuned it, you must finalize your model so that you can make predictions on new data. In this post you will discover how to finalize your machine learning model, save it to file and load it later in order to make predictions on new data. After reading this post you will know How to train a final version of your machine learning model in Weka. How to save your finalized model to file. How to load your finalized model later and use it to make predictions on new data. Weka-ARFF-Viewer-1.png' alt='How To Create Arff File From Excel' title='How To Create Arff File From Excel' />Lets get started. How to Save Your Machine Learning Model and Make Predictions in Weka. Photo by Nick Kenrick, some rights reserved. Tutorial Overview. This tutorial is broken down into 4 parts Finalize Model where you will discover how to train a finalized version of your model. Save Model where you will discover how to save a model to file. Load Model where you will discover how to load a model from file. Make Predictions where you will discover how to make predictions for new data. The tutorial provides a template that you can use to finalize your own machine learning algorithms on your data problems. We are going to use the Pima Indians Onset of Diabetes dataset. Each instance represents medical details for one patient and the task is to predict whether the patient will have an onset of diabetes within the next five years. There are 8 numerical input variables and all have varying scales. You can learn more about this dataset on the UCI Machine Learning Repository. Top results are in the order of 7. We are going to finalize a logistic regression model on this dataset, both because it is a simple algorithm that is well understood and because it does very well on this problem. From millions of real job salary data. Average salary is Detailed starting salary, median salary, pay scale, bonus data report. CRANRBingGoogle. Titlesort Author sort Date of Publication sort AAC No. A hazard in aerobatics Effect on Gforces on pilots FAA 19840228 FSF ALAR Toolkit. Data Source Description Parameters Web URL via HTTP Reads data in commaseparated values CSV, tabseparated values TSV, attributerelation file format ARFF. Download Weka for free. Machine learning software to solve data mining problems. Weka is a collection of machine learning algorithms for solving realworld data. St Johns Fire District Deputy Fire Marshal Community Risk Reduction. The St. Johns Fire District is accepting applications for the full time position of Deputy. After you have found a well performing machine learning model and tuned it, you must finalize your model so that you can make predictions on new data. In this post. The success of molecular modeling and computational chemistry efforts are, by definition, dependent on quality software applications. Open source software development. Issuu is a digital publishing platform that makes it simple to publish magazines, catalogs, newspapers, books, and more online. Easily share your publications and get. How To Create Arff File From Excel' title='How To Create Arff File From Excel' />Need more help with Weka for Machine Learning Take my free 1. Click to sign up and also get a free PDF Ebook version of the course. Start Your FREE Mini Course NowFinalize a Machine Learning Model. Perhaps the most neglected task in a machine learning project is how to finalize your model. Once you have gone through all of the effort to prepare your data, compare algorithms and tune them on your problem, you actually need to create the final model that you intend to use to make new predictions. Finalizing a model involves training the model on the entire training dataset that you have available. Open the Weka GUI Chooser. Click the Explorer button to open the Weka Explorer interface. Load the Pima Indians onset of diabetes dataset from the datadiabetes. Weka Load Pima Indians Onset of Diabetes Dataset. Click the Classify tab to open up the classifiers. Click the Choose button and choose Logistic under the functions group. Select Use training set under Test options. Click the Start button. Weka Train Logistic Regression Model. This will train the chosen Logistic regression algorithm on the entire loaded dataset. It will also evaluate the model on the entire dataset, but we are not interested in this evaluation. It is assumed that you have already estimated the performance of the model on unseen data using cross validation as a part of selecting the algorithm you wish to finalize. It is this estimate you prepared previously that you can report when you need to inform others about the skill of your model. Now that we have finalized the model, we need to save it to file. Save Finalized Model To File. Continuing on from the previous section, we need to save the finalized model to a file on your disk. This is so that we can load it up at a later time, or even on a different computer in the future and use it to make predictions. We wont need the training data in the future, just the model of that data. You can easily save a trained model to file in the Weka Explorer interface. Right click on the result item for your model in the Result list on the Classify tab. Click Save model from the right click menu. Weka Save Model to File. Select a location and enter a filename such as logistic, click the Save button. Your model is now saved to the file logistic. It is in a binary format not text that can be read again by the Weka platform. As such, it is a good idea to note down the version of Weka you used to create the model file, just in case you need the same version of Weka in the future to load the model and make predictions. Generally, this will not be a problem, but it is a good safety precaution. You can close the Weka Explorer now. The next step is to discover how to load up the saved model. Load a Finalized Model. You can load saved Weka models from file. The Weka Explorer interface makes this easy. Open the Weka GUI Chooser. Click the Explorer button to open the Weka Explorer interface. Load any old dataset, it does not matter. We will not be using it, we just need to load a dataset to get access to the Classify tab. If you are unsure, load the datadiabetes. Click the Classify tab to open up the classifiers. Right click on the Result list and click Load model, select the model saved in the previous section logistic. Weka Load Model From File. The model will now be loaded into the explorer. We can now use the loaded model to make predictions for new data. Weka Model Loaded From File Ready For Use. Make Predictions on New Data. We can now make predictions on new data. First, lets create some pretend new data. Make a copy of the file datadiabetes. Open the file in a text editor. Find the start of the actual data in the file with the data on line 9. We only want to keep 5 records. Move down 5 lines, then delete all the remaining lines of the file. The class value output variable that we want to predict is on the end of each line. Delete each of the 5 output variables and replace them with question mark symbols. Weka Dataset For Making New Predictions. Free Lord Your Holy Download Free. We now have unseen data with no known output for which we would like to make predictions. Continue on from the previous part of the tutorial where we already have the model loaded. On the Classify tab, select the Supplied test set option in the Test options pane. Weka Select New Dataset On Which To Make New Predictions. Click the Set button, click the Open file button on the options window and select the mock new dataset we just created with the name diabetes new data. Click Close on the window. Click the More options button to bring up options for evaluating the classifier. Uncheck the information we are not interested in, specifically Output modelOutput per class statsOutput confusion matrixStore predictions for visualizationWeka Customized Test Options For Making Predictions. For the Output predictions option click the Choose button and select Plain. Text. Weka Output Predictions in Plain Text Format. Click the OK button to confirm the Classifier evaluation options. Right click on the list item for your loaded model in the Results list pane. Select Re evaluate model on current test set. Weka Revaluate Loaded Model On Test Data And Make Predictions. The predictions for each test instance are then listed in the Classifier Output pane. Specifically the middle column of the results with predictions like testedpositive and testednegative. You could choose another output format for the predictions, such as CSV, that you could later load into a spreadsheet like Excel. For example, below is an example of the same predictions in CSV format. Objects In Javabean Activation Framework on this page.