Find out how you can use Artificial Intelligence and Machine Learning to automate engineering processes!
There are two ways in which you can use the functionality:
This is a snippet of the functionality’s code. The whole code can be found and downloaded in the VIKTOR GitHub repository:
1@PlotlyView('Models', duration_guess, = 20) #for visualizing the model scores 2def calculate_models(self, params, entity_id, **kwargs): 3 comparison = get_model(params.dataset.data, params.dataset.target, params.choice.toggle) 4 comparison = pd.DataFrame(comparison) 5 cells = [comparison[col] for col in comparison.columns] 6 7fig = go.Figure(data=go.Table(header=dict(values=list(comparison.columns)), cells=dict(values=cells))) 8 9return__PlotlyResult(fig.to_json())
From the code snippet you can see that as little as three parameters can be used to make a machine learning model.
In the video, you can see how a dataset is uploaded, different models and analysis plots are generated and viewed, and how this data is labeled in a VIKTOR application in the demo environment.
The application contains a toggle to distinguish between classification and regression, an input field for the name/path of your .csv file so it can easily find it, a target where the column needs to be learned and distinguished, different analysis plots that can be visualized, and a table input where data ca be inserted, after which the model predicts the new property.
Apply for a demo account to get access to this and all other VIKTOR sample applications.
From the video, you can see that using the machine learning application is easily done in a couple of steps.