Welcome to Cooka
Cooka is a lightweight and visualization system to manage datasets and design model learning experiments through web UI.
It using DeepTables and HyperGBM as experiment engine to complete feature engineering, neural architecture search and hyperparameter tuning automatically.
Features overview
Through the web UI provided by cooka you can:
Add and analyze datasets
Design experiment
View experiment process and result
Using models
Export experiment to jupyter notebook
Screen shots
The machine learning algorithms supported are :
XGBoost
LightGBM
Catboost
The neural networks supported are:
WideDeep
DeepFM
xDeepFM
AutoInt
DCN
FGCNN
FiBiNet
PNN
AFM
The search algorithms supported are:
Evolution
MCTS(Monte Carlo Tree Search)
The supported feature engineering provided by scikit-learn and featuretools are:
- Scaler
StandardScaler
MinMaxScaler
RobustScaler
MaxAbsScaler
Normalizer
- Encoder
LabelEncoder
OneHotEncoder
OrdinalEncoder
- Discretizer
KBinsDiscretizer
Binarizer
- Dimension Reduction
PCA
- Feature derivation
featuretools
- Missing value filling
SimpleImputer
It can also extend the search space to support more feature engineering methods and modeling algorithms.
Read more:
DataCanvas
Cooka is an open source project created by DataCanvas .