Add Weights To ML Model, Then Modify Code For Creating Training Set To Include Weights, -1 Only Used When Action Directly

Mar 9, 2025 by ADMIN 122 views

**Weighted Machine Learning: Enhancing Model Performance with Instance Weights**

Introduction

In machine learning, instance weights are a powerful tool that allows you to assign different levels of importance to specific samples in your training data. This technique is particularly useful when dealing with imbalanced datasets or when certain actions have a greater impact on the outcome. In this article, we will explore how to incorporate instance weights into your machine learning pipeline using ML.NET.

Understanding Instance Weights

Instance weights are a way to give more importance to specific samples in your training data. By assigning weights to each sample, you can influence the model's learning process and improve its performance. In the context of a game like Blackjack, instance weights can be used to prioritize actions that led to wins while discouraging losing moves.

How to Include Weights in Your Training Pipeline

To include weights in your training pipeline, you need to follow these steps:

Step 1: Add a Weight Column to Your Training Data

The first step is to add a new column to your training data that will store the weights. This column should be named "Weight" and should be of type float.

Step 2: Assign Weights Based on the Game Outcome

Next, you need to assign weights to each sample based on the game outcome. Here are some common weight assignments:

1.0: Action led to a win
0.0: Push (neutral outcome)
-1.0: Action directly resulted in a loss

Step 3: Modify Your Pipeline to Include WeightColumnName

Finally, you need to modify your pipeline to include the weightColumnName. This can be done by appending the weightColumnName parameter to the MulticlassClassification.Trainers.OneVersusAll method.

Updated Pipeline with Weights

Here is an updated pipeline that includes weights:

return mlContext.Transforms.Conversion.MapValueToKey("Label", "Action") // Convert Action to categorical key and rename it to "Label"
        .Append(mlContext.Transforms.Conversion.ConvertType("PlayerHandSum", "PlayerHandSum", DataKind.Single))
        .Append(mlContext.Transforms.Conversion.ConvertType("DealerUpCard", "DealerUpCard", DataKind.Single))
        .Append(mlContext.Transforms.Conversion.ConvertType("NumberOfAces", "NumberOfAces", DataKind.Single))
        .Append(mlContext.Transforms.Conversion.ConvertType("CanSplit", "CanSplit", DataKind.Single))
        .Append(mlContext.Transforms.Conversion.ConvertType("CanDouble", "CanDouble", DataKind.Single))
        .Append(mlContext.Transforms.Conversion.ConvertType("DeckCount", "DeckCount", DataKind.Single))
        .Append(mlContext.Transforms.Conversion.ConvertType("Weight", "Weight", DataKind.Single)) // Convert weight to float
        .Append(mlContext.Transforms.Concatenate("Features", "PlayerHandSum", "DealerUpCard", "NumberOfAces", "CanSplit", "CanDouble","DeckCount"))
        .Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(
            mlContext.BinaryClassification.Trainers.FastTree(
                numberOfLeaves: 50, 
                numberOfTrees: 200, 
                learningRate: 0.1
            ), labelColumnName: "Label", weightColumnName: "Weight")) // Use weights in training
        .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedAction", "PredictedLabel")); // Convert back to original label values

How to Assign Weights in Your Training Data

When preparing your dataset, make sure to include a Weight column. Here is an example of how to assign weights to each sample:

var trainingData = new List<BlackJackTrainingRecord>
{
    new BlackJackTrainingRecord { Action = "Hit", PlayerHandSum = 15, DealerUpCard = 10, Weight = -1 },
    new BlackJackTrainingRecord { Action = "Stand", PlayerHandSum = 20, DealerUpCard = 10, Weight = 1 },
    new BlackJackTrainingRecord { Action = "Double", PlayerHandSum = 11, DealerUpCard = 6, Weight = 1 },
    new BlackJackTrainingRecord { Action = "Split", PlayerHandSum = 8, DealerUpCard = 5, Weight = 0 }
};

This approach ensures that the model prioritizes actions that led to wins while discouraging losing moves.

Conclusion

Q: What are instance weights in machine learning?

A: Instance weights are a way to assign different levels of importance to specific samples in your training data. This technique is particularly useful when dealing with imbalanced datasets or when certain actions have a greater impact on the outcome.

Q: Why are instance weights useful?

A: Instance weights are useful because they allow you to influence the model's learning process and improve its performance. By assigning weights to each sample, you can prioritize certain actions or outcomes over others.

Q: How do I assign weights to my training data?

A: To assign weights to your training data, you need to add a new column to your dataset that will store the weights. This column should be named "Weight" and should be of type float. Then, you need to assign weights to each sample based on the game outcome. Here are some common weight assignments:

1.0: Action led to a win
0.0: Push (neutral outcome)
-1.0: Action directly resulted in a loss

Q: How do I modify my pipeline to include weights?

A: To modify your pipeline to include weights, you need to append the weightColumnName parameter to the MulticlassClassification.Trainers.OneVersusAll method. Here is an example of how to do this:

return mlContext.Transforms.Conversion.MapValueToKey("Label", "Action") // Convert Action to categorical key and rename it to "Label"
        .Append(mlContext.Transforms.Conversion.ConvertType("PlayerHandSum", "PlayerHandSum", DataKind.Single))
        .Append(mlContext.Transforms.Conversion.ConvertType("DealerUpCard", "DealerUpCard", DataKind.Single))
        .Append(mlContext.Transforms.Conversion.ConvertType("NumberOfAces", "NumberOfAces", DataKind.Single))
        .Append(mlContext.Transforms.Conversion.ConvertType("CanSplit", "CanSplit", DataKind.Single))
        .Append(mlContext.Transforms.Conversion.ConvertType("CanDouble", "CanDouble", DataKind.Single))
        .Append(mlContext.Transforms.Conversion.ConvertType("DeckCount", "DeckCount", DataKind.Single))
        .Append(mlContext.Transforms.Conversion.ConvertType("Weight", "Weight", DataKind.Single)) // Convert weight to float
        .Append(mlContext.Transforms.Concatenate("Features", "PlayerHandSum", "DealerUpCard", "NumberOfAces", "CanSplit", "CanDouble","DeckCount"))
        .Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(
            mlContext.BinaryClassification.Trainers.FastTree(
                numberOfLeaves: 50, 
                numberOfTrees: 200, 
                learningRate: 0.1
            ), labelColumnName: "Label", weightColumnName: "Weight")) // Use weights in training
        .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedAction", "PredictedLabel")); // Convert back to original label values

Q: What are some common weight assignments?

A: Here are some common weight assignments:

1.0: Action led to a win
0.0: Push (neutral outcome)
-1.0: Action directly resulted in a loss

Q: How do I handle imbalanced datasets with instance weights?

A: To handle imbalanced datasets with instance weights, you can assign higher weights to the minority class. This will help the model to focus on the minority class and improve its performance.

Q: Can I use instance weights with other machine learning algorithms?

A: Yes, you can use instance weights with other machine learning algorithms. However, the specific implementation may vary depending on the algorithm and the library you are using.

Q: What are some best practices for using instance weights?

A: Here are some best practices for using instance weights:

Use instance weights sparingly: Instance weights should be used sparingly and only when necessary. Overusing instance weights can lead to overfitting and poor model performance.
Choose the right weight assignment: Choose the right weight assignment based on the problem you are trying to solve. For example, if you are dealing with an imbalanced dataset, you may want to assign higher weights to the minority class.
Monitor model performance: Monitor model performance and adjust the weight assignment as needed.