Hybrid machine learning
A model is trained through a hybrid machine learning process. In the hybrid machine landing process, an automatic machine learning process is performed on a dataset to generate a model for making a prediction. The automatic machine learning process uses a pipeline to train the model and makes decisions in the steps of the pipeline. After the model is trained through the automatic machine learning process, a representation of the pipeline is generated and presented to a user in a user interface. The user interface allows the user to modify at least some decision made in the automatic machine learning process. One or more modifications are received from the user through the user interface and are used to refine the trained model. The refined model is deployed to make the prediction based on new data.
1. A computer-implemented method, comprising:
receiving a dataset;
generating, automatically and independent of user input, a trained machine learning model using the dataset and an automatic modeling application, the trained machine learning model configured to generate an output based on new data;
displaying, in a user interface of a computing device, a representation of a pipeline that depicts a sequence of decisions made by the automatic modeling application while generating the trained machine learning model using the dataset;
displaying, in the user interface, a plurality of controls, each of the plurality of controls corresponding to at least one decision in the sequence of decisions made by the automatic modeling application while generating the trained machine learning model and configured to receive user input modifying the at least one decision;
receiving, via the user interface, user input to at least one of the plurality of controls modifying one or more decisions in the sequence of decisions; and
causing the automatic modeling application to generate a refined trained machine learning model using a modified sequence of decisions that is defined by the user input to the at least one of the plurality of controls.
2. The computer-implemented method of claim 1 , wherein the sequence of decisions includes a data preparation decision and the modified sequence of decisions comprises a data type setting step in the data preparation decision that is different from the sequence of decisions used to generate the trained machine learning model.
3. The computer-implemented method of claim 1 , wherein the sequence of decisions includes a data preparation decision and the modified sequence of decisions comprises a data encoding step in the data preparation decision that is different from the sequence of decisions used to generate the trained machine learning model.
4. The computer-implemented method of claim 1 , wherein the sequence of decisions includes a data preparation decision and the modified sequence of decisions comprises a data imputation step in the data preparation decision that is different from the sequence of decisions used to generate the trained machine learning model.
5. The computer-implemented method of claim 1 , wherein the sequence of decisions includes a feature engineering decision and the modified sequence of decisions comprises a feature selection step in the feature engineering decision that is different from the sequence of decisions used to generate the trained machine learning model.
6. The computer-implemented method of claim 1 , wherein the sequence of decisions includes a feature engineering decision and the modified sequence of decisions comprises a feature ranking step in the feature engineering decision that is different from the sequence of decisions used to generate the trained machine learning model.
7. The computer-implemented method of claim 1 , wherein the sequence of decisions includes a model training decision and the modified sequence of decisions comprises a hyperparameter tuning step in the model training decision that is different from the sequence of decisions used to generate the trained machine learning model.
8. The computer-implemented method of claim 1 , wherein the sequence of decisions includes a model training decision and the modified sequence of decisions comprises an algorithm selection step in the model training decision that is different from the sequence of decisions used to generate the trained machine learning model.
9. The computer-implemented method of claim 1 , wherein the sequence of decisions includes at least one decision that is selected by the trained machine learning model from a plurality of options and the representation of the pipeline includes a ranking score for each of the plurality of options, wherein the ranking score represents a performance measure of the trained machine learning model resulting from selecting an option for the at least one decision.
10. The computer-implemented method of claim 1 , wherein the representation of the pipeline includes a feature list comprising a plurality of features and a description, for each of the plurality of features, indicating an importance of the feature relative to the output generated by the trained machine learning model, wherein the plurality of features include features used by the automatic modeling application to generate the trained machine learning model.
11. A system comprising:
one or more processors; and
a computer-readable storage medium storing instructions that are executable by the one or more processors to perform operations comprising:
receiving a dataset;
generating, automatically and independent of user input, a trained machine learning model using the dataset and an automatic modeling application, the trained machine learning model configured to generate an output based on new data;
displaying, in a user interface, a representation of a pipeline that depicts a sequence of decisions made by the automatic modeling application while generating the trained machine learning model using the dataset;
displaying, in the user interface, a plurality of controls, each of the plurality of controls corresponding to at least one decision in the sequence of decisions made by the automatic modeling application while generating the trained machine learning model and configured to receive user input modifying the at least one decision;
receiving, via the user interface, user input to at least one of the plurality of controls modifying one or more decisions in the sequence of decisions; and
causing the automatic modeling application to generate a refined trained machine learning model using a modified sequence of decisions that is defined by the user input to the at least one of the plurality of controls.
12. The system of claim 11 , wherein the sequence of decisions includes a data preparation decision and the modified sequence of decisions comprises at least one of a data type setting step, a data encoding step, or a data imputation step in the data preparation decision that is different from the sequence of decisions used to generate the trained machine learning model.
13. The system of claim 11 , wherein the sequence of decisions includes a feature engineering decision and the modified sequence of decisions comprises at least one of a feature selection step or a feature ranking step in the feature engineering decision that is different from the sequence of decisions used to generate the trained machine learning model.
14. The system of claim 11 , wherein the sequence of decisions includes a model training decision and the modified sequence of decisions comprises at least one of a hyperparameter tuning step or an algorithm selection step in the model training decision that is different from the sequence of decisions used to generate the trained machine learning model.
15. The system of claim 11 , wherein the sequence of decisions includes at least one decision that is selected by the trained machine learning model from a plurality of options and the representation of the pipeline includes a ranking score for each of the plurality of options, wherein the ranking score represents a performance measure of the trained machine learning model resulting from selecting an option for the at least one decision.
16. The system of claim 11 , wherein the representation of the pipeline includes a feature list comprising a plurality of features and a description, for each of the plurality of features, indicating an importance of the feature relative to the output generated by the trained machine learning model, wherein the plurality of features include features used by the automatic modeling application to generate the trained machine learning model.
17. A non-transitory computer-readable storage medium storing instructions that are executable by a processing device to perform operations comprising:
receiving a dataset;
generating, automatically and independent of user input, a trained machine learning model using the dataset and an automatic modeling application, the trained machine learning model configured to generate an output based on new data;
displaying, in a user interface, a representation of a pipeline that depicts a sequence of decisions made by the automatic modeling application while generating the trained machine learning model using the dataset;
displaying, in the user interface, a plurality of controls, each of the plurality of controls corresponding to at least one decision in the sequence of decisions made by the automatic modeling application while generating the trained machine learning model and configured to receive user input modifying the at least one decision;
receiving, via the user interface, user input to at least one of the plurality of controls modifying one or more decisions in the sequence of decisions; and
causing the automatic modeling application to generate a refined trained machine learning model using a modified sequence of decisions that is defined by the user input to the at least one of the plurality of controls.
18. The non-transitory computer-readable storage medium of claim 17 , wherein the sequence of decisions includes a data preparation decision and the modified sequence of decisions comprises at least one of a data type setting step, a data encoding step, or a data imputation step in the data preparation decision that is different from the sequence of decisions used to generate the trained machine learning model.
19. The non-transitory computer-readable storage medium of claim 17 , wherein the sequence of decisions includes a feature engineering decision and the modified sequence of decisions comprises at least one of a feature selection step or a feature ranking step in the feature engineering decision that is different from the sequence of decisions used to generate the trained machine learning model.
20. The non-transitory computer-readable storage medium of claim 17 , wherein the sequence of decisions includes a model training decision and the modified sequence of decisions comprises at least one of a hyperparameter tuning step or an algorithm selection step in the model training decision that is different from the sequence of decisions used to generate the trained machine learning model.