MoEv

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]