Class PartialDependencePlotFitDetectorOptions

Namespace

AiDotNet.Models.Options

Assembly

AiDotNet.dll

Configuration options for the Partial Dependence Plot Fit Detector, which uses partial dependence plots to evaluate model fit quality and detect overfitting or underfitting in machine learning models.

public class PartialDependencePlotFitDetectorOptions

Inheritance

object

PartialDependencePlotFitDetectorOptions

Inherited Members

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Remarks

Partial Dependence Plots (PDPs) visualize the relationship between a target variable and a set of input features of interest, marginalizing over the values of all other input features. The Partial Dependence Plot Fit Detector leverages these plots to assess model quality by comparing the complexity and smoothness of the learned relationships. This approach provides valuable insights into whether a model has captured meaningful patterns (good fit), is too simplistic (underfit), or has learned noise in the training data (overfit). By analyzing the characteristics of these plots across different features, the detector can provide early warnings of potential modeling issues that might not be apparent from aggregate metrics alone.

For Beginners: The Partial Dependence Plot Fit Detector helps identify if your model is learning properly by examining how it responds to changes in individual input features.

Imagine you built a model to predict house prices based on features like square footage, location, and age:

• A partial dependence plot shows how the predicted price changes when you vary just one feature (like square footage) while keeping all other features at their average values

These plots help reveal three common problems:

• Overfitting: When your model learns patterns that are too specific to your training data (like a jagged, noisy relationship between square footage and price)
• Underfitting: When your model is too simple and misses important patterns (like a flat line that shows no relationship between square footage and price)
• Good fit: When your model captures meaningful patterns without excess complexity (like a smooth curve showing prices increasing with square footage, but at a decreasing rate)

This class lets you configure how the detector analyzes these plots to automatically identify potential fitting problems in your models.

Properties

NumPoints

Gets or sets the number of points to sample for generating each partial dependence plot.

public int NumPoints { get; set; }

Property Value

int

The number of points to sample, defaulting to 100.

Remarks

This parameter determines the resolution of the partial dependence plots used for fit detection. Each plot is generated by evaluating the model at a specified number of points across the range of each feature of interest. More points provide higher resolution for detecting patterns and irregularities but require more computation. The appropriate number depends on the complexity of the relationships being modeled and the computational resources available. For most applications, 100 points provides a good balance between resolution and computational efficiency.

For Beginners: This setting controls how many sample points are used to create each partial dependence plot.

The default value of 100 means:

• The feature range is divided into 100 equally spaced points
• The model is evaluated at each of these points to create the plot

Think of it like photograph resolution:

• More points (higher resolution) show more detail but require more processing
• Fewer points (lower resolution) are faster but might miss some details

You might want more points (like 200) if:

• You're analyzing complex models with potentially subtle or highly nonlinear relationships
• You need very precise detection of overfitting or underfitting
• You have sufficient computational resources

You might want fewer points (like 50) if:

• You need faster processing
• You're working with many features or a very large dataset
• You're doing preliminary analysis

The right number of points balances detail with computational efficiency. 100 points is sufficient for most applications, capturing meaningful patterns without excessive computation.

OverfitThreshold

Gets or sets the threshold for detecting overfitting based on the variability in partial dependence plots.

public double OverfitThreshold { get; set; }

Property Value

double

The overfit threshold, defaulting to 0.8.

Remarks

This parameter determines the threshold above which a model is considered to be overfitting based on the analysis of its partial dependence plots. Overfitting is detected by measuring the irregularity, jaggedness, and noise in the partial dependence curves. Higher variability between adjacent points in the plot suggests the model may be fitting to noise rather than true patterns in the data. The value represents a normalized score where higher values correspond to stricter detection of overfitting. Appropriate values depend on the domain and the expected smoothness of the true underlying relationships.

For Beginners: This setting determines how "jagged" or "noisy" a partial dependence plot can be before the model is flagged as overfitting.

The default value of 0.8 means:

• If the variability score in the plot exceeds 0.8 (on a scale from 0 to 1)
• The detector will flag the model as potentially overfitting

Think of it like looking at a line representing house prices vs. square footage:

• A smooth line suggests the model has found a general pattern
• A very jagged, zig-zagging line suggests the model is trying to fit every small fluctuation in the data

You might want a higher value (like 0.9) if:

• Your data naturally has some irregular patterns that aren't noise
• You're working in a domain where relationships are inherently complex
• You want to be more conservative about flagging potential overfitting

You might want a lower value (like 0.7) if:

• You strongly prefer simpler, more generalizable models
• Your domain typically has smooth underlying relationships
• You want to be more aggressive in detecting potential overfitting

This threshold helps automate the process of reviewing partial dependence plots, which data scientists often examine visually to assess model quality.

UnderfitThreshold

Gets or sets the threshold for detecting underfitting based on the flatness in partial dependence plots.

public double UnderfitThreshold { get; set; }

Property Value

double

The underfit threshold, defaulting to 0.2.

Remarks

This parameter determines the threshold below which a model is considered to be underfitting based on the analysis of its partial dependence plots. Underfitting is detected by measuring the overall variation and responsiveness of the model to changes in the input features. Flatter, less responsive curves suggest the model may be too simple and failing to capture important relationships in the data. The value represents a normalized score where lower values correspond to stricter detection of underfitting. The appropriate threshold depends on how much feature influence is expected in the domain.

For Beginners: This setting determines how "flat" or "unresponsive" a partial dependence plot can be before the model is flagged as underfitting.

The default value of 0.2 means:

• If the variability score in the plot is below 0.2 (on a scale from 0 to 1)
• The detector will flag the model as potentially underfitting

Continuing with our house price example:

• If changing the square footage barely affects the predicted price (flat line)
• When we know square footage should significantly impact house prices
• This suggests the model is underfitting and not capturing important relationships

You might want a lower value (like 0.1) if:

• Some features genuinely have minimal impact on your target variable
• You're working with many potentially irrelevant features
• You want to be more conservative about flagging potential underfitting

You might want a higher value (like 0.3) if:

• You expect all features to have meaningful impact on your target
• You've already performed feature selection to remove irrelevant variables
• You want to be more aggressive in detecting potential underfitting

This threshold helps identify when your model might be too simple or failing to learn the patterns that exist in your data.