Struct ModelResult<T, TInput, TOutput>

Namespace

AiDotNet.Models.Results

Assembly

AiDotNet.dll

Represents the complete results of a model-building process, including the model solution, fitness metrics, fit detection results, evaluation data, and selected features.

public struct ModelResult<T, TInput, TOutput>

Type Parameters

T

The numeric type used for calculations, typically float or double.

TInput

TOutput

Inherited Members

ValueType.Equals(object)

ValueType.GetHashCode()

ValueType.ToString()

object.Equals(object, object)

object.GetType()

object.ReferenceEquals(object, object)

Remarks

This struct encapsulates all the important information produced during the model-building and evaluation process. It includes the symbolic model itself, a fitness score indicating how well the model performs, detailed fit detection results that analyze potential issues like overfitting or underfitting, comprehensive evaluation data with various performance metrics, and information about which features were selected for the model. This comprehensive package of information allows for thorough analysis and comparison of different models.

For Beginners: This struct is like a container that holds everything about a model's performance.

When building machine learning or statistical models:

• You need to track many different aspects of model performance
• You want to compare different models to choose the best one
• You need to understand not just how well a model performs, but why

This struct stores:

• The actual model solution (the equation or algorithm)
• How well the model fits the data (fitness score)
• Analysis of potential issues like overfitting or underfitting
• Detailed performance metrics on different datasets
• Which input features were used in the model

Having all this information in one place makes it easier to evaluate, compare, and document your models.

Properties

EvaluationData

Gets or sets the detailed evaluation data for the model.

public ModelEvaluationData<T, TInput, TOutput> EvaluationData { readonly get; set; }

Property Value

ModelEvaluationData<T, TInput, TOutput>

A ModelEvaluationData<T> object containing comprehensive performance metrics.

Remarks

This property contains detailed evaluation data for the model, including various performance metrics calculated on different datasets (typically training, validation, and test sets). The evaluation data provides a more comprehensive view of model performance than the single fitness score. It may include metrics such as R-squared, mean squared error, mean absolute error, and others, calculated for each dataset. This detailed information helps in understanding how well the model generalizes to unseen data and in identifying potential issues with the model.

For Beginners: This contains detailed performance metrics for your model.

The evaluation data:

• Provides multiple performance metrics (not just the fitness score)
• Shows how the model performs on different datasets (training, validation, test)
• Gives a more complete picture of model performance

Common metrics included might be:

• R² (R-squared): How much variance is explained
• MSE (Mean Squared Error): Average squared difference between predictions and actual values
• MAE (Mean Absolute Error): Average absolute difference between predictions and actual values
• RMSE (Root Mean Squared Error): Square root of MSE, in the same units as the target variable

This detailed information helps you:

• Better understand model strengths and weaknesses
• Compare models using different metrics
• Assess how well the model will generalize to new data

FitDetectionResult

Gets or sets the results of fit detection analysis.

public FitDetectorResult<T> FitDetectionResult { readonly get; set; }

Property Value

FitDetectorResult<T>

A FitDetectorResult<T> object containing detailed fit analysis.

Remarks

This property contains the results of fit detection analysis, which evaluates how well the model fits the data and identifies potential issues such as underfitting or overfitting. Underfitting occurs when the model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test data. Overfitting occurs when the model is too complex and captures noise in the training data, resulting in good performance on training data but poor performance on test data. The FitDetectorResult includes the type of fit detected, a confidence level for that assessment, and recommendations for improving the model.

For Beginners: This contains an analysis of how well your model fits the data.

The fit detection result:

• Identifies if your model is underfitting, overfitting, or has a good fit
• Provides a confidence level for this assessment
• Offers specific recommendations to improve your model

This analysis helps you understand:

• If your model is too simple (underfitting)
• If your model is too complex (overfitting)
• What steps you should take to improve it

For example, if your model is overfitting, the fit detection might recommend adding regularization or reducing model complexity.

Fitness

Gets or sets the fitness score of the model.

public T Fitness { readonly get; set; }

Property Value

T

A numeric value representing the model's fitness or performance.

Remarks

This property represents the fitness or performance score of the model. The fitness score is a single numeric value that quantifies how well the model performs, typically on the training data. Higher values usually indicate better performance, though the exact interpretation depends on the specific fitness function used. Common fitness metrics include R-squared (coefficient of determination), negative mean squared error, or accuracy. The fitness score is often used as the primary criterion for comparing and selecting models during the model-building process, especially in evolutionary algorithms or other optimization approaches.

For Beginners: This value tells you how well the model performs overall.

The fitness score:

• Measures how well the model fits the data
• Is typically a single number that summarizes performance
• Higher values usually indicate better performance

Common fitness metrics include:

• R² (R-squared): Measures the proportion of variance explained (higher is better)
• Negative MSE (Mean Squared Error): Measures prediction error (closer to zero is better)
• Accuracy: For classification problems, the percentage of correct predictions

This value is important because:

• It allows you to quickly compare different models
• It's often used to guide the model selection process
• It provides a simple summary of model performance

SelectedFeatures

Gets or sets the list of feature vectors selected for the model.

public List<Vector<T>> SelectedFeatures { readonly get; set; }

Property Value

List<Vector<T>>

A list of Vector<T> objects representing the selected features.

Remarks

This property contains the list of feature vectors that were selected for use in the model. Feature selection is the process of identifying and selecting the most relevant features (input variables) for the model, which can improve model performance, reduce overfitting, and enhance interpretability. The selected features are represented as vectors, where each vector corresponds to a feature and contains the values of that feature across all observations in the dataset. This information is useful for understanding which input variables the model considers important and for reproducing the model with new data.

For Beginners: This list shows which input variables were used in the model.

The selected features:

• Represent the input variables that were chosen for the model
• Are stored as vectors (arrays) of values
• May be a subset of all available features if feature selection was performed

Feature selection is important because:

• Using too many features can lead to overfitting
• Some features may be irrelevant or redundant
• Models with fewer features are often more interpretable

For example, if you started with 20 potential input variables but the model only uses 5 of them, this list would contain those 5 selected features.

Solution

public IFullModel<T, TInput, TOutput> Solution { readonly get; set; }

Property Value

IFullModel<T, TInput, TOutput>