How can I save my Weka results?

How can I save my Weka results?

You can easily save a trained model to file in the Weka Explorer interface.

1. Right click on the result item for your model in the “Result list” on the “Classify” tab.
2. Click “Save model” from the right click menu. Weka Save Model to File.
3. Select a location and enter a filename such as “logistic”, click the “Save button.

How do we get training data in Weka?

In the Explorer just do the following:

1. training set: Load the full dataset. select the RemovePercentage filter in the preprocess panel. set the correct percentage for the split.
2. test set: Load the full dataset (or just use undo to revert the changes to the dataset) select the RemovePercentage filter if not yet selected.

How can I check my Weka model?

Train and test set

1. Train and test set. In case you have a dedicated test set, you can train the classifier and then evaluate it on this test set.
2. import weka. core.
3. Instances train = Instances test =
4. Classifier cls = new J48(); cls. buildClassifier(train);
5. Evaluation eval = new Evaluation(train);
6. eval.
7. System.

What is percentage split in Weka?

If we had just one dataset, if we didn’t have a test set, we could do a percentage split. Here’s a percentage split: this is going to be 66% training data and 34% test data. It’s going to make a random split of the dataset. That’s because Weka, before it does a run, re-initializes the random number generator.

How does Weka tool work?

How to Run Your First Classifier in Weka

1. Download Weka and Install. Visit the Weka Download page and locate a version of Weka suitable for your computer (Windows, Mac, or Linux).
2. Start Weka. Start Weka.
3. Open the data/iris. arff Dataset.
4. Select and Run an Algorithm.
5. Review Results.

Why is cross-validation better than simple train test split?

Cross-validation is usually the preferred method because it gives your model the opportunity to train on multiple train-test splits. This gives you a better indication of how well your model will perform on unseen data. That makes the hold-out method score dependent on how the data is split into train and test sets.

How do I start the Weka tool?

How to do principal component analysis in Weka stack?

Set the filter to PrincipalComponents and and the classifier to whatever classifier you want to use. Train it on the un-transformed training set and you’ll be able to just feed it the untransformed test set. If you really need the modified test set I’d recommend using the knowledge flow tool to make something like this:

How to apply PCA but keep original attributes?

If I load my data and select PrincipalComponent in the Preprocess tab, it applies PCA, but to all of the attributes. So “id” becomes incorporated which produces nonsense results. It also replaces my original data with the principal components produced.

What’s the best way to ask a Weka question?

Asking for help, clarification, or responding to other answers. Making statements based on opinion; back them up with references or personal experience. To learn more, see our tips on writing great answers. Not the answer you’re looking for? Browse other questions tagged data-mining weka pca or ask your own question.

When to use scale and translation in Weka?

Scale is the length of number line and translation is the lower bound. This filter get applied to all numeric columns. You can’t selectively normalize. Load iris dataset. Used when dataset known to be in Gaussian (bell curve) distribution. This filter get applied to all numeric columns. You can’t selectively standardize. Load diabetes dataset.