MoEv is a general-purpose tool that allows for building classification models from labeled datasets MoEv. In addition, MoEv allows for performing data cleaning and pre-processing operations. It has been successfully used in different research areas such as jamming attacks detection on real-time location systems, academic success prediction at educational institutions or to detect network attacks.
The models that can be trained in MoEv are as follows:
| Models |
|---|
| Adaptive Boosting |
| Bagging Classifier |
| Bernoulli Restricted Boltzmann Machine |
| Classification And Regression Tree |
| K-Nearest Neighbors |
| Linear Discriminant Analysis |
| Naive Bayes |
| One-vs-the-rest |
| Quadratic Discriminant Analysis |
| Random Forest |
| Stochastic Gradient Descent |
The internal scheme of MoEv is as follows:
Once the tool is used, the output that MoEv offers is similar to the following:
KNeighbors_Classifier.joblib
Accuracy for model is: 0.964116
Classification report:
precision recall f1-score support
0 0.987845 0.942207 0.964486 198137
1 0.941017 0.987583 0.963738 184986
micro avg 0.964116 0.964116 0.964116 383123 macro avg 0.964431 0.964895 0.964112 383123 weighted avg 0.965235 0.964116 0.964125 383123
Confusion Matrix:
[[186686 11451] [ 2297 182689]]
MoEv can be downloaded from the following link [1]
