Predictive models can be saved and re-used. Models are saved in Python pickle format.
Models first require data for training. They output a trained model, which can be saved with Save Model widget in the pickle format.
Load in Python¶
Models can also be imported directly into Python and used in a script.
import pickle with open('model.pkcls', 'rb') as model: lr = pickle.loads(model) lr >> LogisticRegressionClassifier(skl_model=LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1.0, l1_ratio=None, max_iter=10000, multi_class='auto', n_jobs=1, penalty='l2', random_state=0, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False))