Introduction¶
MLToolKit supports all stages of the machine learning application development process.
Functions¶
- Data Extraction (SQL, Flatfiles, Binary Files, Images, etc.)
- Exploratory Data Analysis (statistical summary, univariate analysis, visulize distributions, etc.)
- Feature Engineering (Supports numeric, text, date/time. Image data support will integrate in later releases of v0.1)
- Model Building (Currently supported for binary classification and regression only)
- Hyper Parameter Tuning [in development for v0.2]
- Cross Validation (will integrate in later releases of v0.1)
- Model Performance Analysis, Explain Predictions (LIME and SHAP) and Performance Comparison Between Models.
- JSON input script for executing model building and scoring tasks.
- Model Building UI [in development for v0.2]
- ML Model Building Project [in development for v0.2]
- Auto ML (automated machine learning) [in development for v0.2]
- Model Deploymet and Serving [included, will be imporved for v0.2]
Supported Machine Learning Algorithms/Packages¶
- RandomForestClassifier: scikit-learn
- LogisticRegression: statsmodels
- Deep Feed Forward Neural Network (DFF): tensorflow
- Convlutional Neural Network (CNN): tensorflow
- Gradient Boost : catboost, xgboost, lightgbm
- Linear Regression: statsmodels
- RandomForestRegressor: scikit-learn
… More models will be added in the future releases …