Table of Contents

Class FitnessCalculatorOptions

Namespace
AiDotNet.Models.Options
Assembly
AiDotNet.dll

Configuration options for the fitness calculator, which determines how model performance is evaluated.

public class FitnessCalculatorOptions
Inheritance
FitnessCalculatorOptions
Inherited Members

Remarks

The fitness calculator is responsible for computing a score that represents how well a model fits the data or makes predictions. Different metrics emphasize different aspects of model performance, such as overall fit, error magnitude, or prediction accuracy.

For Beginners: Think of the fitness calculator as a judge that scores how well your AI model is performing. Just like different sports have different scoring systems (points in basketball, goals in soccer), AI models can be evaluated using different metrics. Some metrics focus on how close your predictions are to the actual values, while others might focus on whether your model captures the overall patterns in the data. These options let you choose which scoring system to use and how to interpret the scores.

Properties

ScoreType

Gets or sets the type of metric used to calculate the fitness score.

public FitnessCalculatorType ScoreType { get; set; }

Property Value

FitnessCalculatorType

The score type, defaulting to R-squared.

Remarks

Different metrics evaluate different aspects of model performance:

  • R-squared measures the proportion of variance explained by the model (higher is better, max 1.0)
  • MeanSquaredError measures the average squared difference between predictions and actual values (lower is better)
  • MeanAbsoluteError measures the average absolute difference between predictions and actual values (lower is better)
  • RootMeanSquaredError is the square root of MeanSquaredError, which gives errors in the same units as the target variable (lower is better)

For Beginners: This determines which method is used to score your model's performance. The default, R-squared (also written as R²), measures how well your model explains the variations in your data. An R-squared of 1.0 means your model perfectly predicts every value, while 0.0 means it's no better than just guessing the average value every time. Other options include:

  • Mean Squared Error: Measures the average of the squared differences between predictions and actual values. It penalizes larger errors more heavily.
  • Mean Absolute Error: Measures the average of the absolute differences between predictions and actual values. It treats all sizes of errors equally.
  • Root Mean Squared Error: The square root of Mean Squared Error, which gives you an error value in the same units as your original data.
Choose R-squared when you want to understand how much of the data variation your model explains. Choose one of the error metrics when you want to focus on the size of prediction errors.

UseMaximumValue

Gets or sets whether higher values indicate better fitness.

public bool UseMaximumValue { get; set; }

Property Value

bool

True if higher values are better, defaulting to true.

Remarks

Some metrics like R-squared are better when higher (with a maximum of 1.0), while others like Mean Squared Error are better when lower (with a minimum of 0.0). This property determines how the scores should be interpreted when comparing models or evaluating performance.

For Beginners: This tells the system whether a higher score means better performance or worse performance. With the default value of true, the system assumes that higher scores are better (which is correct for R-squared). If you switch to an error-based metric like Mean Squared Error, you should set this to false because lower errors mean better performance. Think of it like golf versus basketball scoring - in golf, lower scores are better, but in basketball, higher scores win. This setting helps the system know which direction is "better" when comparing models.

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