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4. $k$-nearest neighbor classifier [@Chandrashekar1997] which is a class of supervised learning method based on the similarity of training examples. It uses a collection of training examples to recognize input data. 5. Linear regression model [@Wikipedia:LinearRegression] which is a type of mathematical regression technique. Its goal is to find the best fitting line (or plane) between two variables. Linear regression model is a type of statistical model that describes the relationship between two variables. 6. Support vector machine (SVM) [@Wikipedia:SVM] which is a learning machine based on the principle of structural risk minimization [@Vapnik:1995:NSL:1703038.1703045]. It can be applied to various classification problems. 7. Decision trees [@Wikipedia:DecisionTrees] which are models for explaining data. The decision trees based on the principle of a simple decision that can be used to predict the outcome of a test of a system on a set of data. We used RapidMiner[^1] to construct a data mining model based on the features described above, using the first ten listed features by default. The rapidminer supports a linear regression model and a decision tree model, among many others. Hyperparameter Optimization --------------------------- The selected five features that are expected to be useful for classification were incorporated into a hyperparameter optimization model using the grid-based parameter search method in RapidMiner. We used the default RapidMiner settings for hyperparameter optimization. We started with the number of bins of each feature as the range of the possible hyperparameter. For example, we started with a possible value of $5$ for the number of bins in the histogram feature. Then we performed a grid search on the values that were tested in each feature: an integer value from $1$ to $5$, with a step size of $1$. If the linear regression model could not fit the training data for any value of a hyperparameter, then it was eliminated. In our case, none of the models could fit the data for all features. Thus, we were unable to build the learning model. We iterated through the other settings to find the value that provided the best fit. Interpretation of Results ------------------------- Once we were able to build the learning model

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