class Cadmium::Classifier::Tabular::KNN

• Cadmium::Classifier::Tabular::KNN
• Reference
• Object

Overview

K-Nearest Neighbors classifier for multi-feature tabular data.

This classifier stores all training data and makes predictions by finding the k most similar training examples and taking a majority vote.

Features

• Handles numerical features of any dimension
• Supports multiple distance metrics (Euclidean, Manhattan, Cosine, etc.)
• No training phase - just data storage
• Suitable for small to medium datasets

Example

classifier = Cadmium::Classifier::Tabular::KNN.new(k: 3)

features = [
  [1.0, 2.0, 3.0],
  [1.1, 2.1, 3.1],
  [5.0, 6.0, 7.0],
]
labels = ["class_a", "class_a", "class_b"]

classifier.train(features, labels)

# Predict new sample
result = classifier.classify([1.05, 2.05, 3.05])
# => "class_a"

# Get detailed results with vote counts
details = classifier.classify_details([1.05, 2.05, 3.05])
# => {"class_a" => 3, "class_b" => 0}

Defined in:

Constructors

• .load_model(path : String) : self

  Load a trained model from a file.

• .new(k : Int32 = 5, distance_metric : DistanceMetric = DistanceMetric::Euclidean)

Instance Method Summary

• #classify(features : Array(Float64)) : String

  Classify a new sample and return the predicted label.

• #classify_batch(features_batch : Array(Array(Float64))) : Array(String)

  Classify multiple samples at once.

• #classify_details(features : Array(Float64)) : Hash(String, Int32)

  Classify a new sample and return detailed vote counts.

• #distance_metric : DistanceMetric

  Distance metric to use for finding nearest neighbors

• #k : Int32

  Number of neighbors to consider

• #save_model(path : String) : Nil

  Save the trained model to a file.

• #train(features : Array(Array(Float64)), labels : Array(String)) : self

  Train the classifier by storing feature vectors and labels.

• #train(features : Array(Float64), label : String) : self

  Train with a single sample.

Constructor Detail

classifier = Cadmium::Classifier::Tabular::KNN.load_model("knn_model.msgpack")

Instance Method Detail

classifier.classify([1.0, 2.0, 3.0]) # => "class_a"

results = classifier.classify_batch([[1.0, 2.0], [3.0, 4.0]])
# => ["class_a", "class_b"]

details = classifier.classify_details([1.0, 2.0, 3.0])
# => {"class_a" => 3, "class_b" => 2}

classifier.save_model("knn_model.msgpack")

features = [[1.0, 2.0], [3.0, 4.0]]
labels = ["a", "b"]
classifier.train(features, labels)

classifier.train([1.0, 2.0, 3.0], "class_a")