In this project, we would tackle three different parts using Python programming language and JupyterLab. The first part is focusing on programming KNNs (K-nearest neighbors) and NBs (Naive Bayes) from scratch. Then, we would move on afterward to comparing the results obtained by these both algorithms for final evaluation. Therefore, we would consider which one is performing the best.
In the second part, we would use sklearn library to compare the two algorithms on a larger dataset, specifically in four different settings: Influence of reduced training set, influence of large training set, influence of absence of a teacher
and unknown distribution.
In the third part, we would compare the same algorithms for image classification on 10 different classes, using feature descriptors.
Table of Contents
- Introduction
- 1. Programming of a discrimination method: KNNs (K-nearest neighbors) and Naïve Bayes (NB)
- 1.1 Data
- 1.2 Implementation of KNNs Algorithm
- 1.3 Implementation of NBs Algorithm
- 1.4 Experiment and Results
- 2. Comparison of the two methods (parametric and nonparametric)
- 2.1 Influence of the size of the training set: the case of reduced size
- 2.2 Influence of the size of the training set: the case of large size
- 2.3 In the case of the absence of a teacher
- 2.4 Distribution unknown
- 3. Approach based on Descriptors
- 3.1 Calculation of descriptors
- 3.2 Implementation of a classification system
- Conclusion
- References
Objectives and Key Themes
This project aims to implement and compare the K-Nearest Neighbors (KNN) and Naïve Bayes (NB) algorithms for classification tasks. The project uses Python and JupyterLab for implementation and analysis. The comparison considers various factors impacting algorithm performance. * Implementation of KNN and NB algorithms from scratch. * Comparative analysis of KNN and NB performance across different datasets and settings. * Evaluation of algorithm performance using error rate and visualization techniques. * Exploration of the influence of training set size and the presence/absence of a teacher on classification accuracy. * Application of the algorithms to image classification using feature descriptors.Chapter Summaries
Introduction: This introductory section outlines the project's three main parts: implementing KNN and NB algorithms, comparing their performance under various conditions (including dataset size and the presence of a teacher), and applying them to image classification using feature descriptors. It sets the stage for the detailed explorations to follow in subsequent chapters.
1. Programming of a discrimination method: KNNs (K-nearest neighbors) and Naïve Bayes (NB): This chapter details the programming of both KNN and NB algorithms from scratch. It begins by describing the dataset – 300 data points, half of which are used for training, with classification based on distance to known points. The implementation of the KNN algorithm involves calculating Euclidean distances, identifying nearest neighbors, and determining the most frequent class among them. The NB algorithm implementation is based on Bayes' Theorem, employing a decision rule based on training parameters. The chapter concludes with an analysis of the algorithms' performance, showing how increasing the number of neighbors (k) in KNN improves accuracy up to a certain point.
2. Comparison of the two methods (parametric and nonparametric): This chapter expands on the comparison of KNN and NB algorithms, using the sklearn library on a larger dataset. Four scenarios are explored: reduced training set size, large training set size, absence of a teacher (unsupervised learning), and unknown data distribution. The chapter would likely discuss how the algorithms' performance changes under each scenario, highlighting their strengths and weaknesses in different contexts, possibly illustrating this with statistical measures and figures which are not included in this preview.
3. Approach based on Descriptors: This chapter focuses on applying KNN and NB to image classification. It describes the process of calculating feature descriptors from images and then implementing a classification system based on these descriptors. This section would delve into specific descriptor types and the methodology used to apply the algorithms to image data, likely comparing their accuracy and efficacy in this higher-dimensional setting. The exact details of the descriptors and the classification methodology are not available in this preview.
Keywords
K-Nearest Neighbors (KNN), Naïve Bayes (NB), classification algorithms, machine learning, pattern recognition, error rate, feature descriptors, image classification, data analysis, Python, JupyterLab, algorithm comparison, training set size, supervised learning, unsupervised learning.
Frequently Asked Questions: A Comprehensive Language Preview of KNN and Naïve Bayes Algorithms
What is the main topic of this document?
This document is a comprehensive preview of a project comparing the K-Nearest Neighbors (KNN) and Naïve Bayes (NB) algorithms for classification tasks. It covers the algorithms' implementation, a comparative analysis under various conditions, and their application to image classification.
What algorithms are compared in this project?
The project focuses on comparing the performance of the K-Nearest Neighbors (KNN) and Naïve Bayes (NB) algorithms.
What programming languages and tools are used?
The project utilizes Python and JupyterLab for implementation and analysis.
What are the key objectives of this project?
The main objectives include implementing KNN and NB from scratch, comparing their performance across different datasets and settings, evaluating performance using error rate and visualization, exploring the influence of training set size and the presence/absence of a teacher, and applying the algorithms to image classification using feature descriptors.
How are the algorithms implemented?
The KNN algorithm is implemented by calculating Euclidean distances, identifying nearest neighbors, and determining the most frequent class. The Naïve Bayes algorithm is implemented based on Bayes' Theorem, using a decision rule based on training parameters. Initially, a smaller dataset of 300 points (half for training) is used for testing and implementing these algorithms from scratch. Later, the `sklearn` library is employed for a broader comparison using larger datasets.
What factors are considered when comparing the algorithms?
The comparison considers several factors, including the size of the training set (both reduced and large sizes), the presence or absence of a teacher (supervised vs. unsupervised learning), and the handling of unknown data distributions.
How is the performance of the algorithms evaluated?
Algorithm performance is evaluated using error rates and visualization techniques. Specific details regarding the visualization methods are not included in this preview. In the initial implementation, increasing the number of neighbors (k) in KNN is observed to improve accuracy up to a certain point.
How are the algorithms applied to image classification?
The algorithms are applied to image classification by using feature descriptors. The specific types of descriptors and the methodology for applying the algorithms to image data are not detailed in this preview.
What are the key takeaways from the chapter summaries?
The introduction outlines the project's structure. Chapter 1 details the implementation of KNN and NB. Chapter 2 compares their performance under various conditions. Chapter 3 applies them to image classification using feature descriptors. The specific findings and results from each chapter are only broadly summarized in this preview.
What are the key words associated with this project?
Key words include: K-Nearest Neighbors (KNN), Naïve Bayes (NB), classification algorithms, machine learning, pattern recognition, error rate, feature descriptors, image classification, data analysis, Python, JupyterLab, algorithm comparison, training set size, supervised learning, unsupervised learning.
- Arbeit zitieren
- Marwan Al Omari (Autor:in), 2021, Understanding of Algorithms. KNNs and Naive Bayes, München, GRIN Verlag, https://www.grin.com/document/1215098
