ML.NET Tutorial | Get started in 10 minutes

Intro

Purpose

Install the ML.NET CLI, then train and use your first machine learning model with ML.NET.

Prerequisites

None.

Time to Complete

10 minutes + download/installation time

Scenario

An app that can predict whether the text from customer reviews is negative or positive sentiment.

Download and install

Install .NET SDK

To build .NET apps, you need to download and install the .NET 8 SDK (Software Development Kit).

Install .NET 8 SDK instructions

Install ML.NET CLI

The ML.NET command-line interface (CLI), provides tools for building machine learning models with ML.NET.

Note: Currently, ML.NET CLI is in Preview and only supports the latest LTS version of the .NET SDK (.NET 8).

FOR x64 MACHINES - Run the following command:

Terminal

dotnet tool install -g mlnet-linux-x64

FOR ARM64 CHIP ARCHITECTURES - Run the following command instead:

Terminal

dotnet tool install -g mlnet-linux-arm64

If the tool installs successfully, you should see the following output message where [arch] is the chip architecture:

Terminal

You can invoke the tool using the following command: mlnet
Tool 'mlnet-linux-[arch]' (version 'X.X.X') was successfully installed.

Note: If you're using a console other than Bash (for example, zsh, which is the new default for macOS), then you'll need to give mlnet executable permissions and include mlnet to the system path. Instructions on how to do this should appear in the terminal when you install mlnet (or any global tool). In general, the following command should work for most systems: chmod +x [PATH-TO-MLNET-CLI-EXECUTABLE]

Alternatively, you can try using the following command to run the mlnet tool:

Terminal

~/.dotnet/tools/mlnet

If the command still gives you an error, use the I ran into an issue button below to report the issue and get help fixing the problem.

Create your app

In your terminal, run the following commands:

Terminal

mkdir myMLApp
cd myMLApp

The mkdir command creates a new directory named myMLApp, and the cd myMLApp command puts you into the newly created app directory.

Your model training code will be generated in the upcoming steps.

Pick a scenario

To generate your model, you need to select your machine learning scenario.

There are several ML scenarios that are supported by the ML.NET CLI:

Classification - Use this when you want to predict which category data belongs in (for example, analyzing sentiment of customer reviews as either positive or negative).
Image classification - Use this when you want to predict which category an image belongs to (for example, predicting if an image is of a cat or a dog).
Regression (for example, value prediction) - Use this when you want to predict a numeric value (for example, predicting house price).
Forecasting - Use this when you want to forecast future values in a time-series (for example, forecast quarterly sales).
Recommendation - Use this when you want to recommend items to users based on historical ratings (for example, product recommendation).

In this case, you'll predict sentiment based on the content (text) of customer reviews, so you'll use classification.

Download and add data

Download the Sentiment Labelled Sentences datasets from the UCI Machine Learning Repository. Unzip sentiment labelled sentences.zip and save the yelp_labelled.txt file to the myMLApp directory.

Each row in yelp_labelled.txt represents a different review of a restaurant left by a user on Yelp. The first column represents the comment left by the user, and the second column represents the sentiment of the text (0 is negative, 1 is positive). The columns are separated by tabs, and the dataset has no header. The data looks like the following:

yelp_labelled.txt

Wow... Loved this place.	        1
Crust is not good.	        0
Not tasty and the texture was just nasty.	        0

Train your model

Now, you'll train your model with the yelp_labelled.txt dataset.

In your terminal, run the following command (in your myMLApp folder):

Terminal

mlnet classification --dataset "yelp_labelled.txt" --label-col 1 --has-header false --name SentimentModel  --train-time 60

What do these commands mean?

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.

--dataset: You chose yelp_labelled.txt as the dataset (internally, the CLI will split the one dataset into training and testing datasets).
--label-col: You must specify the target column you want to predict (or the Label). In this case, you want to predict the sentiment in the second column (zero-indexed columns means this is column "1").
--has-header: Use this option to specify if the dataset has a header. In this case, the dataset doesn't have a header, so it's false.
--name: Use this option to provide a name for your machine learning model and related assets. In this case, all assets associated with this machine learning model will have SentimentModel in the name.
--train-time: You must also specify the amount of time you'd like the ML.NET CLI to explore different models. In this case, 60 seconds (you can try increasing this number if no models are found after training). Note that for larger datasets, you should set a longer training time.

Progress

While the ML.NET CLI is exploring different models, it displays the following data:

Start training - This section shows each model iteration, including the trainer (algorithm) used and evaluation metrics for that iteration.
Time left - This and the progress bar will indicate how much time is left in the training process in seconds.
Best algorithm - This shows you which algorithm has performed the best so far.
Best score - This shows you the performance of the best model so far. Higher accuracy means the model predicted more correctly on test data.

If you want, you can view more information about the training session in the log file generated by the CLI.

Evaluate your model

After the ML.NET CLI selects the best model, it will display the training Summary, which shows you a summary of the exploration process, including how many models were explored in the given training time.

Top models

While the ML.NET CLI generates code for the highest performing model, it also displays the top models (up to 5) with the highest accuracy that it found in the given exploration time. It displays several evaluation metrics for those top models, including AUC, AUPRC, and F1-score. For more information, see ML.NET metrics.

Generate code

The ML.NET CLI adds both the machine learning model and the code for training and consuming the model, which includes the following:

A new directory called SentimentModel is created containing a .NET console app that includes the following files:
- Program.cs: This file contains code to run the model.
- SentimentModel.consumption.cs: This file contains the model input and output classes and a Predict method that can be used for model consumption.
- SentimentModel.mbconfig: This file is a JSON file that keeps track of the configurations and results from your training.
- SentimentModel.training.cs: This file contains the training pipeline (data transforms, algorithm, and algorithm parameters) used to train the final model.
- SentimentModel.zip: This file is the trained ML.NET model, which is a serialized zip file.

To try the model, you can run the console app to predict the sentiment of a single statement with the model.

Consume your model

The ML.NET CLI has generated the trained model and code for you, so you can now use the model in .NET applications (for example, your SentimentModel console app) by following these steps:

In the command line, navigate to the consumeModelApp directory.
Terminal
```
cd SentimentModel
```

Open the Program.cs in any code editor and inspect the code. The code should look similar to the following:

Program.cs

using System;

namespace SentimentModel.ConsoleApp
{
    class Program
    {
        static void Main(string[] args)
        {
            // Add input data
            SentimentModel.ModelInput sampleData = new SentimentModel.ModelInput()
            {
              Col0 = @"Wow... Loved this place."
            };

            // Make a single prediction on the sample data and print results
            var predictionResult = SentimentModel.Predict(sampleData);

            Console.WriteLine("Using model to make single prediction -- Comparing actual Col1 with predicted Col1 from sample data...\n\n");


            Console.WriteLine($"Col0: @{"Wow... Loved this place."}");
            Console.WriteLine($"Col1: {1F}");


            Console.WriteLine($"\n\nPredicted Col1: {predictionResult.PredictedLabel}\n\n");
            Console.WriteLine("=============== End of process, hit any key to finish ===============");
            Console.ReadKey();
        }
    }
}

Run your SentimentModel.ConsoleApp. You can do this by running the following command in the terminal (make sure you are in the SentimentModel directory):

Terminal

dotnet run

The output should look something like this:

Terminal

Using model to make single prediction -- Comparing actual Col1 with predicted Col1 from sample data...


Col0: Wow... Loved this place.
Col1: 1
Class                          Score
-----                          -----
1                              0.9651076
0                              0.034892436
=============== End of process, hit any key to finish ===============

Next steps

Congratulations, you've built your first machine learning model with the ML.NET CLI!

Now that you've used the ML.NET CLI for Classification (specifically sentiment analysis), you can try other scenarios. Try out a Regression scenario (specifically price prediction) using the Taxi Fare dataset to keep building ML.NET models with the ML.NET CLI.

Download the Taxi Fare dataset

ML.NET for Beginners

Let Luis introduce you to the concept of machine learning & AI, explain what you can do with it, and guide you on how to get started with OpenAI, Azure AI Services, and ML.NET:

ML.NET CLI Docs

Learn more about ML.NET CLI

ML.NET samples

Explore the ML.NET samples on GitHub

Developer docs

Dig deeper with the documentation for ML.NET

ML.NET Tutorial - Get started in 10 minutes

Intro

Purpose

Prerequisites

Time to Complete

Scenario

Download and install

Already have Visual Studio 2022?

Upgrade to the latest version of Model Builder

Check for Visual Studio updates

Install .NET SDK

Install ML.NET CLI

Create your app

Add machine learning

Pick a scenario

Download and add data

Add data

Train your model

Training results

What do these commands mean?

Progress

Evaluate your model

Try out your model

Top models

Generate code

Consume your model

Next steps

ML.NET for Beginners

Model Builder guide

ML.NET samples

Developer docs

ML.NET for Beginners

ML.NET CLI Docs

ML.NET samples

Developer docs

Report an issue

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