MoEv

MoEv is a general-purpose tool for building classification models from labeled datasets developed in Python. MoEv provides the following functionalities: data cleaning, normalization, dimensionality reduction, and hyperparameter optimization. This optimization is developed through the Grid-SearchCV method and also through DaskGridSearchCV. Dask GridSerarchCV provides advanced parallelism especially useful when using MoEv on a supercomputer. MoEv trains evaluate and obtain a report of supervised and semi-supervised/unsupervised learning models. The report includes highly relevant information such as Accuracy, Precision, Recall, F1-Score, FAR, and the confusion matrix. The latest release of the tool accepts as input CSV files or images. 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
Bagging Classifier
One-class SVM
Isolation Forest
Local Outlier Factor

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]