Complete The Following Assignment In One MS Word Docu 960488

Complete The Following Assignment In One Ms Word Documentwrite Post 1

Complete the following assignment in one MS word document: Write post 1 (Chapter 5): What is the relationship between Naïve Bayes and Bayesian networks? What is the process of developing a Bayesian networks model? Your answer should be words There must be at least one APA formatted reference (and APA in-text citation) to support the thoughts in the post. Do not use direct quotes, rather rephrase the author's words and continue to use in-text citations. Write post 2 (Chapter 6): List and briefly describe the nine-step process in conducting a neural network project. Your answer should be words There must be at least one APA formatted reference (and APA in-text citation) to support the thoughts in the post. Do not use direct quotes, rather rephrase the author's words and continue to use in-text citations.

Paper For Above instruction

Understanding Naïve Bayes and Bayesian Networks, and the Neural Network Development Process

Introduction

The fields of machine learning and artificial intelligence (AI) utilize various probabilistic and neural models for making predictions and uncovering patterns within data. Among these, Naïve Bayes classifiers and Bayesian networks are prominent tools based on probabilistic reasoning, while neural networks are vital for modeling complex, nonlinear relationships. This paper explores the relationship between Naïve Bayes and Bayesian networks, details the process of developing a Bayesian network model, and outlines the nine-step process involved in conducting a neural network project.

Relationship Between Naïve Bayes and Bayesian Networks

Naïve Bayes classifiers are a simplified form of Bayesian networks, which are probabilistic graphical models representing a set of variables and their conditional dependencies via a directed acyclic graph (DAG). The key similarity lies in their foundation on Bayes' theorem, used to compute posterior probabilities based on prior information and likelihoods (Kohavi & John, 1997). Naïve Bayes assumes that all features are conditionally independent given the class label, drastically simplifying the network structure to a single node (for the class) influencing all feature nodes. Conversely, Bayesian networks do not impose this independence assumption, allowing for more complex interdependencies among variables, which makes them more flexible but computationally more intensive (Pearl, 1988). Essentially, Naïve Bayes can be viewed as a specific, simplified case of Bayesian networks where the conditional independence assumptions lead to a particular, easy-to-construct structure.

Process of Developing a Bayesian Networks Model

Developing a Bayesian networks model involves several systematic steps. The process begins with problem formulation, where the goal and scope of the model are clearly defined (Koller & Friedman, 2009). The next step involves identifying relevant variables that influence the problem. Data collection and preprocessing follow, ensuring data quality and suitability for modeling. Structuring the network then entails defining the nodes and their connections—either through expert knowledge or data-driven algorithms such as constraint-based or score-based methods. This is followed by parameter learning, where the conditional probability distributions governing each node are estimated from data, often using maximum likelihood estimation or Bayesian methods. Model validation and testing are critical, involving assessing the model's accuracy and robustness against unseen data, and making adjustments as necessary. Finally, deployment involves applying the Bayesian network to decision-making tasks, and ongoing monitoring ensures its relevance and effectiveness over time. Throughout this process, domain expertise, statistical techniques, and iterative validation play crucial roles in crafting a reliable and useful model (Heckerman, Heckerman, & Geiger, 1995).

Neural Network Project: Nine-Step Process

Conducting a neural network project encompasses a structured nine-step process aimed at ensuring efficient and effective model development. The first step is defining the problem and establishing project objectives, which sets the scope and success criteria. The second step involves data collection and preprocessing, ensuring data quality through cleaning, normalization, and feature selection. The third step focuses on designing the network architecture—choosing the number of layers, nodes, and activation functions suited for the problem. The fourth step is initializing the network parameters, such as weights and biases, before training begins. The fifth step is training the network using algorithms such as backpropagation combined with gradient descent, where the network learns by minimizing the error between predicted and actual outputs. The sixth step involves validating the model using a separate dataset to tune hyperparameters and prevent overfitting. The seventh step tests the finalized network on unseen data to evaluate its generalization performance. The eighth step includes deploying the trained model in a real-world setting for decision support or prediction. The final ninth step involves ongoing monitoring and maintenance, updating the model as new data becomes available or as environmental conditions change (Hagan, Demuth, & Beale, 2014).

Conclusion

Understanding the distinctions and connections between probabilistic models like Naïve Bayes and Bayesian networks enhances their appropriate application in data analysis. Simultaneously, mastering the structured approach to neural network development ensures efficient and effective machine learning projects. These methods, supported by systematic processes and theoretical foundations, continue to advance AI capabilities across industries.

References

• Hagan, M. T., Demuth, H. B., & Beale, M. H. (2014). Neural network design. CRC press.
• Heckerman, D., Heckerman, D., & Geiger, D. (1995). Learning Bayesian networks: The combination of knowledge and statistically derived data. Machine Learning, 20(3), 197-243.
• Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. MIT press.
• Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1-2), 273-324.
• Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann.