Ryan is a 12 year old diagnosed with Oppositional Defiant Disorder (ODD). As a small child, Ryan was first diagnosed with Attention Deficit, Hyperactivity Disorder. He had just started preschool and was unable to remain in one place for more than a minute or two. His parents had already had a very difficult time in disciplining him, because he would become very upset and throw temper tantrums when he could not do as he wished. He just seemed to be an unhappy, irritable child. This behavior continued in school. As time progressed, Ryan was diagnosed with Oppositional Defiant Disorder, because he continued to refuse to listen to adults and comply with the rules.By the age of 12, he has begun to bully and annoy others. His parents have decided that the approaches they have used in the past are not working, and they are more afraid his behavior will only get worse.Provide explanations for these questions and statements:Describe the behaviors apparent for each disorder in this scenario (Attention Deficit Hyperactivity Disorder and Oppositional Defiant Disorder).If this behavior becomes worse and Ryan starts to violate the rights of others, what medical diagnosis will be given and why?Discuss at least two types of medication appropriate to treat Ryan and support your choices with rationale and credible resourcesDescribe why therapy for the parent is important in this scenario including at least two supporting rationales.

A.        Independently locate a SOCIAL WORK article which presents an actual example of using a SSD to evaluate social work practice. You can pick ANY intervention and ANY problem.   Write out the complete APA style reference to your paper (include linkable DOI if available), and prepare written answers to the questions noted below.  A SOCIAL WORK article is either one appearing in a SOCIAL WORK journal (not counseling, psychology, medicine, etc.) OR which is authored/co-authored by one or more professional social workers.  A SOCIAL WORKER is someone with a social work degree (e.g., BSW, MSW, Ph.D. in social work).  No credit will be given for reporting on a non-social work article.  Locate an article NOT previously mentioned in our textbook or other work.  The article you locate should clearly involve making use of a SSD, and present the data in the form of the line graph, and data from individual clients.  Do not report a study involving large numbers of people, or that makes use of a survey methodology, needs assessment, client satisfaction, pre-, quasi- or true experimental design, or solely qualitative analysis. Answer the following questions about your article. 1.         What was the issue/problem being addressed? 2.         Who were the clients? 3.         How as the problem/issue assessed?  Was this approach reliable? Valid? 4.         What was the intervention?  Were there any fidelity checks? 5.         What was the type of SSD used to evaluate outcomes? 6.         What were the apparent results and how persuaded were you of the authors’ conclusions? 7.         What was a limitation/weakness of this study?  A strength?  8.         How could this study have been improved? B.        Pick a clinical problem or issue of interest to you, and design an experimental (e.g., ABA, ABAB, MBL) SSD evaluation of an intervention of interest to you, in determining the effects of the treatment with a single client, group, family or couple.  Use the following headings in preparing your response: Clinical Situation            (Describe the issue or problem the client(s) are bringing to you) Outcome Measure(s) (Describe how you will assess and measure the clients’ issue, repeatedly over time.  Defend your choice of outcome measure, by describing its practicality, reliability and validity, how you will obtain the requested information, direct measures of behavior, rapid assessment instrument, reports of others, client self-report, physiological measures, etc.).   Intervention(s) (Describe your intervention here.  Ideally you will locate and apply an intervention which has credible research support.  Provide supportive citations from the empirical literature to justify your choice of interventions) Single System Research Design (Describe your chosen single system design.  I want you to use an experimental design, which means it must include withdrawal phases and/or multiple baselines which are staggered.  Use withdrawal phases if you have an intervention which effects are NOT likely to produce enduring change, and a multiple baseline design for treatments whose effects are anticipated to be more durable).  Include at least 5 data points per phase.   Ethics              (Describe why you believe that the approach you have planned is ethically appropriate) Graph              (Prepare a computer-generated graph of your design, containing hypothetical data             depicting a strong effect of treatment, e.g., it really looks like the treatment was effective.  

Discussion 1:MeredithProPrediction of class in the test data set is fast and easyNaïve Bayes performs well with categorical variables as well as numerical. Only a small amount of training data is required in order to run the algorithm ConIf a category exists in the test data that was not in the training data, then it will be assigned a probability of 0.Naïve Bayes assumes that predictors are independent, but in actuality it is rare for all predictors to be completely independent of one another.Known to be a poor estimator.The first Pro is regarding speed and ease of use of the algorithm. There are many algorithms that are slow (neural networks) and/or complex (random forest). Having an algorithm that can be run quickly saves time and sanity. Next, the Naïve Bayes can handle numerical variables, but has to create a normal distribution for them. It can handle categories best, and when classifying data, the information is typically in categorical form. Lastly, by being able to limit the amount of training data needed, you can potentially avoid overfitting (Ray, 2017).For the Cons, forcing a probability of 0 to any data is not beneficial to the overall outcome. It’s much better to have a probability assigned to each possible category. The algorithm is much faster than others, however that is due in part to the fact that it assumes predictors are independent. This makes sense because the model is based on Bayes’ Theorem for conditional probability. It’s very rare in life for predictors to be independent of one another. Because the model assumes predictors are independent, it is known to be inaccurate (Yildirim, 2020).Ray, S. (September 11, 2017). 6 easy steps to learn naïve bayes algorithm with codes in Python and R. Analytics Vidhya. Retrieved on November 22, 2021 from https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/Yildirim, S. (February 14, 2020). Naïve Bayes classifier – explained. Towards Data Science. Retrieved on November 22, 2021 from https://towardsdatascience.com/naive-bayes-classifier-explained-50f9723571edDiscussion 2: TylerPros of the Naïve Bayes classifier:It is a very fast algorithm that can "easily predict the class of a test dataset" (Vadapalli, 2020).It can be used to solve multi-class prediction problems.It performs better than many other models in cases where less data is available if the assumption of independence of features holds (Vadapalli, 2020).Performs exceptionally well with categorical input variables.Cons of the Naïve Bayes classifierIf you add a new category variable after training the model it will be assign to zero probability and will not make proper predictions. This is often referred to as 'Zero Frequency' (Vadapalli, 2020).This model is not a great estimator.The model runs off of the assumption that all features are independent of one another which is rarely realistic but is situationally acceptable.Regarding the pros of the Naïve Bayes classifier model, it is clear how many of these make this model a great choice in many circumstances. The speed of a model means that less resources are necessary to run the model efficiently. Its strength as a predictor stands out especially over models when less data is available which may be the case for a lot of relatively new project. It is a big deal for companies to experience these benefits with a classification model.There are always circumstantial downsides to every machine learning model and while this model experiences several that seem like big deals, there are still cases where these downsides can be overcome. For example, when the zero frequency problem occurs within your dataset, this problem can be overcome through data smoothing (Vadapalli, 2020). Knowing that this model is not a great estimator is also important so that you don't rely on it for that purpose. Regarding the final con, it can seem like a very large downside to be required to assume that all of your features are independent of one another but in many circumstances this may not be a deal breaker. Especially in the case that you're working on a classification problem with a relatively small dataset. In a case like this, the pros may likely outweigh the cons.ReferencesVadapalli, P. (2020, December 7). Naive Bayes classifier: Pros & Cons, applications & types explained. upGrad blog. Retrieved November 26, 2021, from https://www.upgrad.com/blog/naive-bayes-classifier/. Discussion 3: CornilusA Naive Bayes classifier is a probabilistic machine learning model that’s used for classification task. The crux of the classifier is based on the Bayes theorem. Using Bayes theorem, we can find the probability of A happening, given that B has occurred. Here, B is the evidence and A is the hypothesis. The assumption made here is that the predictors/features are independent. That is presence of one particular feature does not affect the other. Hence it is called naive.Advantages of Naïve Bayes Classifier1. When assumption of independent predictors holds true, a Naive Bayes classifier performs better as compared to other models.2. Naive Bayes requires a small amount of training data to estimate the test data. So, the training period is less.3. Naive Bayes is also easy to implement.Disadvantages of Naïve Bayes Classifier1. Main imitation of Naive Bayes is the assumption of independent predictors. Naive Bayes implicitly assumes that all the attributes are mutually independent. In real life, it is almost impossible that we get a set of predictors which are completely independent.2. If categorical variable has a category in test data set, which was not observed in training data set, then model will assign a 0 (zero) probability and will be unable to make a prediction.Naive Bayes algorithms are mostly used in sentiment analysis, spam filtering, and recommendation systems etc. They are fast and easy to implement but their biggest disadvantage is that the requirement of predictors to be independent. In most of the real life cases, the predictors are dependent, this hinders the performance of the classifier.The Naive Bayes model is called "Naive" precisely because it assumes features are conditionally independent from one another despite the fact that this assumption very rarely holds. However, the Naive Bayes model can still achieve strong classification accuracy even in cases where its conditional independence assumption is significantly inaccurate.ReferencesThe Professionals Point: Advantages and Disadvantages of Naive Bayes in Machine LearningNaive Bayes Classifier. What is a classifier? | by Rohith Gandhi | Towards Data Science