owing formula:

3.  Then identify the hyperplane. The regular parameter *C* is a penalty factor, which can balance the model complexity and empirical risk. In addition, ε*~i~* indicates the positive parameters called slack variables, which represent the distance between the misclassified sample and the optimal hyperplane.

4.  Forecast the classification of the test dataset using hyperplane and support vectors

### Bagging

1.  Generate a new training set by sampling from the original training set

2.  Repeat step 1 N times to get the N new training sets, and train N trees in N different training sets

3.  Calculate the classification results by averaging the predicted value of each tree or use the majority

4.  Out-of-bag error estimation: The data not sampled in step 1 is used as the test set of the corresponding generated tree to evaluate the predicted results

### Random Forest

1.  Create a new training set from a sample of a training set

2.  Repeat step 1 N times to get N new training sets, and train N trees on the training sets

3.  Identify the optimal candidate node as the prediction space from the randomly selected m feature set when building the tree model

### Boosting

1.  Initialize the weight vector of the training data

2.  Construct m weak classifiers

3.  Calculate the classification error rate of the m weak classifiers

4.  If one sample is misclassified, its weight will be increased, and the next weak classifier pays more attention to this sample; otherwise, its weight will be decreased.

5.  After all the weak classifiers finish the training, the stronger classifier is constructed.

Results
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Metrics
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Precision, recall, and F measure are the metrics that are used to evaluate our proposed ML methods. Based on the classification of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN), we have the following formulas.

We then define the F measure, a metric that balances precision and recall.

Evaluation
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We calculate the overall acc