Classifier Comparison Guide

Not sure which classifier to use? This guide compares all four classifiers across accuracy, speed, storage, and capabilities to help you make the right choice.

Quick Decision Guide

Need real-time classification (<1ms)?     → Naive Bayes or Logistic Regression
Need to find similar documents?           → LSI or KNN
Need semantic search?                     → LSI
Need the best classification accuracy?    → Logistic Regression
Have very little training data (<500)?    → Naive Bayes
Need feature importance / explainability? → Logistic Regression
Want the simplest solution?               → Naive Bayes

At a Glance

Detailed Comparison

Primary Purpose

Typical Accuracy

Training Performance

Classification Performance

Storage Requirements

Robustness

Unique Capabilities

Real-World Benchmark

Performance on a 10,000 document spam classification task:

When to Use Each Classifier

Naive Bayes

Choose Naive Bayes when you need:

• Maximum classification speed
• Streaming or incremental training
• Simple, low-resource deployment
• Quick prototyping

# Great for: spam filters, real-time classification
classifier = Classifier::Bayes.new 'Spam', 'Ham'
classifier.train(spam: spam_emails, ham: good_emails)
classifier.classify(incoming_email)  # ~0.05ms

Avoid when: You need semantic understanding or finding similar documents.

Logistic Regression

Choose Logistic Regression when you need:

• Best classification accuracy
• Well-calibrated probability scores
• Feature importance analysis
• Confidence thresholds for decisions

# Great for: sentiment analysis, high-stakes classification
classifier = Classifier::LogisticRegression.new 'Positive', 'Negative', 'Neutral'
classifier.train(positive: good_reviews, negative: bad_reviews, neutral: meh_reviews)
classifier.probabilities(review)  # => {"Positive" => 0.82, "Negative" => 0.12, "Neutral" => 0.06}
classifier.weights(:positive, limit: 10)  # Top 10 words indicating positive

Avoid when: You have very small training sets or need incremental learning.

KNN (k-Nearest Neighbors)

Choose KNN when you need:

• Classification AND similarity search
• Explainable results (“similar to these documents”)
• Multi-label classification
• Incremental updates

# Great for: recommendation systems, tag suggestions
knn = Classifier::KNN.new(k: 5, weighted: true)
knn.add(tech: tech_articles, sports: sports_articles)
result = knn.classify_with_neighbors(new_article)
# => {category: "Tech", confidence: 0.85, neighbors: [...]}

Avoid when: You have large datasets (>10K documents) or need real-time speed.

LSI (Latent Semantic Indexing)

Choose LSI when you need:

• Semantic search across documents
• Finding related or similar content
• Document clustering
• Understanding word relationships

# Great for: search engines, content discovery
lsi = Classifier::LSI.new
lsi.add(
  "Ruby" => "Ruby is a dynamic programming language",
  "Python" => "Python is great for data science"
)
lsi.search("programming languages")  # Semantic search
lsi.find_related("Ruby article")     # Find similar documents

Avoid when: You only need classification - use Bayes or LogReg instead.

Decision Flowchart

                         START
                           |
          Do you need to find similar documents?
                    /              \
                  YES              NO
                   |                |
     Do you also need         Do you need
     clustering/search?       real-time speed?
          /      \               /      \
        YES      NO            YES      NO
         |        |             |        |
        LSI      KNN            |    Is accuracy
                                |    critical?
                                |      /    \
                                |    YES    NO
                                |     |      |
                          Need calibrated    |
                          probabilities?     |
                             /    \          |
                           YES    NO         |
                            |      |         |
                         LogReg  Bayes    Bayes

Summary

Next Steps

• Bayes Basics - Get started with Naive Bayes
• Logistic Regression - Learn about calibrated classification
• KNN Basics - Explore instance-based classification
• LSI Basics - Dive into semantic analysis
• Ensemble Classifier Tutorial - Combine multiple classifiers for even better results