Python-flask based web-application which provides fitting and predicting regression using Random Forest and Gradient Boosting which are implemented from scratch.
numpyscipysklearnflaskflask_wtfwtforms
You need to upload .csv file with dataset for training. Since my app doesn't perform data preprocessing, your dataset must contain only numerical values. Additionaly, you can upload validation dataset. You must provide name of target column.
You can specify parameters for model chosen. Random Forest model must be provided with following parameters:
n_estimators: number of trees to ensemble, integer valuemax_depth: limitation on max depth of tree, integer valuesubspace_size: fraction of features selected to train a single tree, float in(0, 1].
In addition, Gradient Boosting requires learning_rate, positive float.
After clicking Fit! button you need to wait until your model is trained. It may take long time.
After completing model training, learning curve for train dataset is shown (and for validation dataset, if provided). RMSE is used.
If you want to make predictions with trained model, click on Predict? button.
You need to upload .csv file with dataset for prediction. It must have all columns named as in training datasets and not contain target column.
After clicking Predict button you need to wait until predictions are made. It may take long time.
After making prediction, it is shown in scrollable table. You can download it as .csv file.
