MulticlassClassification_Iris

Iris Classification

ML.NET version	API type	Status	App Type	Data type	Scenario	ML Task	Algorithms
v1.0.0-preview	Dynamic API	Up-to-date	Console app	.txt files	Iris flowers classification	Multi-class classification	Sdca Multi-class

In this introductory sample, you'll see how to use ML.NET to predict the type of iris flower. In the world of machine learning, this type of prediction is known as multiclass classification.

Problem

This problem is centered around predicting the type of an iris flower (setosa, versicolor, or virginica) based on the flower's parameters such as petal length, petal width, etc.

To solve this problem, we will build an ML model that takes as inputs 4 parameters:

• petal length
• petal width
• sepal length
• sepal width

and predicts which iris type the flower belongs to:

• setosa
• versicolor
• virginica

To be precise, the model will return probabilities for the flower to belong to each type.

ML task - Multiclass classification

The generalized problem of multiclass classification is to classify items into one of three or more classes. (Classifying items into one of the two classes is called binary classification).

Some other examples of multiclass classification are:

• handwriting digit recognition: predict which of 10 digits (0-9) an image contains.
• issues labeling: predict which category (UI, back end, documentation) an issue belongs to.
• disease stage prediction based on patient's test results.

The common feature for all those examples is that the parameter we want to predict can take one of a few (more that two) values. In other words, this value is represented by enum, not by integer, float/double or boolean types.

Solution

To solve this problem, first we will build an ML model. Then we will train the model on existing data, evaluate how good it is, and lastly we'll consume the model to predict an iris type.

1. Build model

Building a model includes:

• Uploading data (iris-train.txt with DataReader)
• Create an Estimator and transform the data to one column so it can be used effectively by an ML algorithm (with Concatenate)
• Choosing a learning algorithm (StochasticDualCoordinateAscent).

The initial code is similar to the following:

    // STEP 1: Common data loading configuration
    let trainingDataView = mlContext.Data.LoadFromTextFile<IrisData>(trainDataPath, hasHeader = true)
    let testDataView = mlContext.Data.LoadFromTextFile<IrisData>(testDataPath, hasHeader = true)

    // STEP 2: Common data process configuration with pipeline data transformations
    let dataProcessPipeline = 
        EstimatorChain()
            .Append(mlContext.Transforms.Conversion.MapValueToKey("LabelKey","Label"))
            .Append(mlContext.Transforms.Concatenate("Features", "SepalLength",
                                                     "SepalWidth",
                                                     "PetalLength",
                                                     "PetalWidth"))
            .AppendCacheCheckpoint(mlContext)

    // STEP 3: Set the training algorithm, then append the trainer to the pipeline  
    let trainer = mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy(labelColumnName = "LabelKey", featureColumnName = "Features")
    let trainingPipeline = dataProcessPipeline.Append(trainer)

2. Train model

Training the model is a process of running the chosen algorithm on a training data (with known iris types) to tune the parameters of the model. It is implemented in the Fit() method from the Estimator object.

To perform training we just call the method providing the training dataset (iris-train.txt file) in a DataView object.

    let trainedModel = trainingPipeline.Fit(trainingDataView)

3. Evaluate model

We need this step to conclude how accurate our model operates on new data. To do so, the model from the previous step is run against another dataset that was not used in training (iris-test.txt). This dataset also contains known iris types. MulticlassClassification.Evaluate calculates the difference between known types and values predicted by the model in various metrics.

    let metrics = mlContext.MulticlassClassification.Evaluate(predictions, "Label", "Score")

    Common.ConsoleHelper.printMultiClassClassificationMetrics (trainer.ToString()) metric

To learn more on how to understand the metrics, check out the Machine Learning glossary from the ML.NET Guide or use any available materials on data science and machine learning.

If you are not satisfied with the quality of the model, there are a variety of ways to improve it, which will be covered in the examples category.

4. Consume model

After the model is trained, we can use the Predict() API to predict the probability that this flower belongs to each iris type.

    // Create prediction engine related to the loaded trained model
    let predEngine = trainedModel.CreatePredictionEngine<IrisData, IrisPrediction>(mlContext)

    //Score sample 1
    let resultprediction1 = predEngine.Predict(DataStructures.SampleIrisData.Iris1)

    printfn "Actual: setosa.     Predicted probability: setosa:      %.4f" resultprediction1.Score.[0]
    printfn "                                           versicolor:  %.4f" resultprediction1.Score.[1]
    printfn "                                           virginica:   %.4f" resultprediction1.Score.[2]
    printfn ""

Where SampleIrisData.Iris1 stores the information about the flower we'd like to predict the type for.

module SampleIrisData =
    let Iris1 = { IrisData.Empty with SepalLength = 5.1f; SepalWidth = 3.3f; PetalLength = 1.6f; PetalWidth= 0.2f}
    (...)