Under Active Development
A lightweight library for creating, training, and deploying machine learning models designed to work in an intensive environment. Please note, that the list of realized functionality is subject to change.
- Linear Regression
- Logistic Regression
- k-nearest neighbors (kNN)
- Naive Bayes (Multinomial)
- Decision Tree
- Random Forest
- Gradient Boosting
- KernelSVM (Linear, Polynomial, RBF) [BETA]
- Adam Optimizer
- Gradient Descent
- L1 Regularization
- L2 Regularization
- ElasticNet Regularization
- GridSearchCV
- Logistic Loss Function
- Mean Squared Error
- CrossValidator
- Accuracy
- Precision
- F1-Score
- Recall
- Label Encoder
- One Hot Encoder
- Tfidf Encoder
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August 2024:
Model saving and loading. -
September 2024:
First stable release.