Now you'll train your model with the
Model Builder evaluates many models with varying algorithms and settings to give you the best performing model.
Leave the Time to train, which is the amount of time you'd like Model Builder to explore various models, as 10 seconds. Note that for larger datasets, you should set a longer training time.
Select Start Training to start the training process. Once training starts, you can see the time remaining.
Once training is done, you can see a summary of the training results.
If you want, you can view more information about the training session in the Machine Learning Output window.
After model training finishes, go to the Evaluate step.
In your terminal, run the following command (in your
mlnet classification --dataset "wikipedia-detox-250-line-data.tsv" --label-col "Sentiment" --train-time 10
mlnet classification command runs ML.NET with AutoML to explore many iterations of classification models with varying combinations of data transformations, algorithms, and algorithm options and then chooses the highest performing model.
wikipedia-detox-250-line-data.tsvas the dataset (internally, the CLI will split the one dataset into training and testing datasets).
While the ML.NET CLI is exploring different models, it displays the following data:
If you want, you can view more information about the training session in the log file generated by the CLI.