![]() ![]() The only features we know today are: the player’s age, his (her) role, the match date, and his (her) belonging to the home team.Įverything else is unknown: how many minutes he (she) will play, how many goals or self-goals he (she) will score, if he (she) will receive a yellow or a red card, if the home team will win, etc. Let’s suppose we want to make a prediction on a match that player X will play tomorrow. But in our specific case (and more in general), do we know what we want to extract from our data? Are our features good enough?Ī good feature has a known value at prediction time: we can’t create a model with a full set of data and instead make a prediction with missing information. In every Machine Learning course, it’s evident what we want to predict: the flat price per square meter, the taxi tips in New York, etc. ![]() If you have any questions or want to chat with me, feel free to contact me via EMAIL or social media.Let’s continue from the previous article, talking about features, the properties we use as input to our model in order to build a prediction. In this article, you learned how to use ML.NET to create a Credit Card Fraud detection model. NET package that allows you to create and use Machine Learning models inside. ITransformer mlModel = pipeline.Fit(CreateEmptyDataView()) įor more information on using a Tensorflow Model in ML.NET, check out 'Run with ML.NET C# code a TensorFlow model exported from Azure Cognitive Services Custom Vision'. var dataProcessPipeline = ("Features", new , Therefore I chose to keep it simple and only concatenated all features (this must always be done in ML.NET). However, it is hard to do any preprocessing for this particular data-set because it has 28 anonymous features. To create and train a model using ML.NET, we need to create a pipeline containing the needed data-preprocessing and the training algorithm. IDataView trainingDataView = (ĪllowSparse: false) Creating and training a model Now that we have both the input and output data modeled, we can load our actual data into memory using the LoadFromTextFile method. The score field denotes the output in percent while the prediction field is a boolean. This can be done in a similar fashion as the above script. Now that we have our data modeled, we need to also model what our output should look like. The important thing is to specify the right data-type and column index. Here we create a field for each of the columns inside our data-set. Namespace CreditCardFraudDetection.DataModels Figure 2: Credit Card Fraud Detection data-set using Microsoft.ML.Data The class of the transaction, either 0 or 1, the amount of the transaction, the time the transaction occurred, and 28 anonymous features. We will use the Credit Card Fraud Detection data-set for this article, which can be freely downloaded on Kaggle. Inside this file, we state all the columns of our data-set. To do so, we create a file called ModelInput.cs inside a folder called DataModels. Loading in and preprocessing a data-set in ML.NET is quite different from working with other machine learning packages/frameworks because it requires us to state our data structure explicitly. Figure 1: Installing ML.NET Loading in a data-set and creating a data pipeline Depending on your use case you might need to also install some extra packages like Microsoft.ML.ImageAnalytics, Microsoft.ML.TensorFlow or Microsoft.ML.OnnxTransformer. The only thing needed is to install is the Microsoft.ML package. ![]() Adding ML.NET to a C# projectĪdding ML.NET to your C# or F# project is fairly straightforward. The complete code for the binary classification model can be found on my Github. In this article, I will show you how to use ML.NET to create a binary classification model, discuss its AutoML capabilities and show you how to use a Tensorflow model with ML.NET. To use the power of Machine Learning in C#, Microsoft created a package called ML.NET, which provides all the basic Machine Learning functionality. One of the most popular languages today is C# which is used for many applications. ![]() This can be because of one of many reasons, including already having a code-base in another language or having no experience in Python or R. However, sometimes an individual or company can't or doesn't want to use Python or R. These two languages support every common machine learning algorithm, preprocessing technique, and much more and can be used for almost every machine learning problem. When thinking of data science and machine learning, two programming languages, Python and R, immediately come to mind. ![]()
0 Comments
Leave a Reply. |