You can tune samples to improve measures such as accuracy, Area Under ROC Curve (AUC), and Mean Square Error (MSE) by changing the values of the hyper parameter. For more information on tuning hyper parameters, see How to Tune Hyper Parameters. Tuned samples for well known data sets that are shipped with WSO2 Machine Learner are as follows.

- Generating a Tuned Model Using the Decision Tree Algorithm
- Generating a Tuned Model Using the Lasso Regression Algorithm
- Generating a Tuned Model Using the Linear Regression Algorithm
- Generating a Tuned Model Using the Logistic Regression LBFGS Algorithm
- Generating a Tuned Model Using the Logisitc Regression SGD Algorithm
- Generating a Tuned Model Using the Naive Bayes Algorithm
- Generating a Tuned Model Using the Random Forest Algorithm
- Generating a Tuned Model Using the Ridge Regression Algorithm
- Generating a Tuned Model Using the Support Vector Machine Algorithm
- Generating a Tuned Model Using the K Means Anomaly Detection Algorithm with Labeled Data

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