Table of Contents

Class Sample<T>

Namespace
AiDotNet.LinearAlgebra
Assembly
AiDotNet.dll

Represents a single data sample consisting of features and a target value for machine learning algorithms.

public class Sample<T>

Type Parameters

T

The data type of the target value and feature vector elements (e.g., float, double).

Inheritance
Sample<T>
Inherited Members

Remarks

For Beginners: A Sample is like a single example that we use to train or test a machine learning model.

Think of it this way: if you were teaching someone to identify fruits, each Sample would be one fruit. The "Features" would be the characteristics you observe (color, size, shape, texture), and the "Target" would be the correct answer (apple, banana, orange).

For instance, in a house price prediction model:

  • Features might include: number of bedrooms, square footage, neighborhood rating, etc.
  • Target would be the actual price of the house

The machine learning algorithm learns from many of these samples to make predictions on new data.

Constructors

Sample(Vector<T>, T)

Initializes a new instance of the Sample class with the specified features and target.

public Sample(Vector<T> features, T target)

Parameters

features Vector<T>

The feature vector containing input values.

target T

The target value or label for this sample.

Remarks

For Beginners: This is the constructor method that creates a new Sample object. When you want to create a new sample in your code, you would use this method by providing:

  1. A vector of features (the input characteristics)
  2. A target value (the correct answer)

For example:

// Create a feature vector for a house with 3 bedrooms, 1500 sq ft, and 10 years old
var features = new Vector<double>([3, 1500, 10]);

// The house price is $250,000
double price = 250000;

// Create a sample with these features and target
var houseSample = new Sample<double>(features, price);

Properties

Features

Gets or sets the feature vector containing the input values for this sample.

public Vector<T> Features { get; set; }

Property Value

Vector<T>

Remarks

For Beginners: Features are the measurable properties or characteristics of what we're studying. They are the inputs that our machine learning model uses to make predictions.

A Vector is simply a collection of numbers arranged in a specific order. In this case, each number in the vector represents a different feature or characteristic of our sample.

For example, if we're predicting house prices:

  • Features[0] might be the number of bedrooms
  • Features[1] might be the square footage
  • Features[2] might be the age of the house
  • And so on...

The model learns how these features relate to the target value during training.

Target

Gets or sets the target value (or label) for this sample.

public T Target { get; set; }

Property Value

T

Remarks

For Beginners: The Target is the correct answer or outcome that we're trying to predict.

In supervised learning, we provide both the features and the target to the algorithm during training. The algorithm learns to predict the target based on the features.

For example:

  • In a house price prediction model, the target would be the actual price of the house.
  • In an email spam filter, the target would be whether the email is "spam" or "not spam".
  • In a medical diagnosis system, the target might be whether a patient has a certain condition.

After training, when we only have features (inputs) but don't know the target, the model can make predictions for us.

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