-train-time: You must also specify the amount of time you would like the ML.NET CLI to explore different models.In this case, you want to predict the sentiment in the second column (zero-indexed columns means this is column "1"). -label-col: You must specify the target column you want to predict (or the Label).In this case, the dataset doesn't have a header, so it's false. -has-header: You use this option to specify if the dataset has a header.-dataset: You chose yelp_labelled.txt as the dataset (internally, the CLI will split the one dataset into training and testing datasets).The mlnet classification command runs ML.NET with AutoML to explore many iterations of classification models in the given amount of train time with varying combinations of data transformations, algorithms, and algorithm options and then chooses the highest performing model. If you want, you can view more information about the training session in the Machine Learning Output window.Īfter model training finishes, go to the Evaluate step.Ĭommand prompt Copy mlnet classification -dataset "yelp_labelled.txt" -has-header false -label-col 1 -train-time 60 What do these commands mean? Models explored (total) - This shows you the total number of models explored by Model Builder in the given amount of time.Training time - This shows you the total amount of time that was spent training / exploring models.Best model - This shows you which algorithm performed the best during Model Builder's exploration.Higher accuracy means the model predicted more correctly on test data. Best accuracy - This shows you the accuracy of the best model that Model Builder found.Once training is done, you can see a summary of the training results. Once training starts, you can see the time remaining. Select Start training to start the training process. Model Builder automatically adjusts the training time based on the dataset size. Note that for larger datasets, the training time will be longer. Model Builder evaluates many models with varying algorithms and settings based on the amount of training time given to build the best performing model.Ĭhange the Time to train, which is the amount of time you'd like Model Builder to explore various models, to 60 seconds (you can try increasing this number if no models are found after training).
You can update the Feature columns and modify other data loading options in Advanced data options, but it is not necessary for this example.Īfter adding your data, go to the Train step. In this case, the review comment column ("col0") is the Feature column. All of the columns in the dataset besides the Label are automatically selected as Features. The columns that are used to help predict the Label are called Features. The Label is what you're predicting, which in this case is the sentiment found in the second column ("col1") of the dataset. Under Column to predict (Label), select "col1". Since your dataset does not have a header, headers are auto-generated ("col0" and "col1"). Once you select your dataset, a preview of your data appears in the Data Preview section. Select File as the input data source type.īrowse for yelp_labelled.txt. In this case, you'll add yelp_labelled.txt from a file. In Model Builder, you can add data from a local file or connect to a SQL Server database. Model Builder will guide you through the process of building a machine learning model in the following steps. The mbconfig file is simply a JSON file that keeps track of the state of the UI. In the Add New Item dialog, make sure Machine Learning Model (ML.NET) is selected.Ĭhange the Name field to SentimentModel.mbconfig and select the Add button.Ī new file named SentimentModel.mbconfig is added to your solution and the Model Builder UI opens in a new docked tool window in Visual Studio.Right-click on the myMLApp project in Solution Explorer and select Add > Machine Learning. Visual Studio creates your project and loads the Program.cs file. NET 5.0 (Current) as the Target Framework. Make sure Place solution and project in the same directory is unchecked.Select the C# Console Application project template.Select Create a new project from the Visual Studio 2019 start window.