Class TabularClassifier and TabularRegressor are designed for automated generate best performance shallow/deep architecture
for a given tabular dataset. (Currently, theis module only supports lightgbm classifier and regressor.)
clf = TabularClassifier(verbose=True)
clf.fit(x_train, y_train, time_limit=12 * 60 * 60, data_info=datainfo)- x_train: string format text data
- y_train: int format text label
- data_info: a numpy.array describing the feature types (time, numerical or categorical) of each column in x_train.
Notes: Preprocessing of the tabular data:
- Class
[TabularPreprocessor]involves several automated feature preprocessing and engineering operation for tabular data . *The input data should be in numpy array format for the classTabularClassifierandTabularRegressor.