Open diabetes.arff (attached) in a text editor (Notepad++) and read about its attributes. Once you understand what the attributes represent, open the data set in Weka. Run the following classifiers using the default algorithm values and 10-folds cross validation: J48, Nearest Neighbor, Naïve Bayes, and Artificial Neural Network (ANN). Note the accuracies of each algorithm in the table below.
Discretize the diastolic blood pressure (pres), BMI (mass), and age attributes using the values shown in the tables below. Create a new ARFF file with this data in it and name it diabetes_disc.arff. Include a screenshot of each attribute’s distribution in Weka after you have performed discretization on those attributes. Be sure to properly label each screenshot.
Diastolic Blood Pressure
low: < 90 ideal: 90 to 120 prehigh: > 120 to 140
high: > 140
Body Mass Index (BMI)
underweight: < 18.5 normal: 18.5 to 25 overweight: > 25
young: < 40 middle: 40 to 60 elderly: > 60
Using the discretized data set, rerun J48, Nearest Neighbor, Naïve Bayes, and ANN and note their accuracies in the table below. How did the accuracies of each classifier change from the previous data set to now? Did discretization improve classifier performance or not for these classifiers?
Using the Nearest Neighbor classifier on the continuous data set (diabetes.arff), change the k-value to 3, 5, 7, and 9 and note the resultant accuracies in the table below. What happens to the classifier’s accuracy as k increases? Why might this happen?
Critical criminology has gained traction in recent years, with its devotion to questioning the definitions of crime and measurements of official statistics, its critical view of agents, systems, and institutions of social control, and the connections with social justice and policy change (Carrington & Hogg, 2002). Theories of critical criminology are rooted in the structure of society, focusing on power systems and inequality. This paper will focus on labeling theory and crimes of the powerful, as they have a certain dichotomy regarding public vs. private criminality. With labeling theory, those in power have the authority to decide what is the “norm” and what is the “other,” ostracizing the “other” from the rest of society. The stigmatization of public shaming for the common citizen is carried out in all aspects of public life – the labeled individual is looked down on by family, peers, community, and employers, and it is very hard for them to shake the label (Denver et al., 2017; Kroska et al., 2016). Regarding crimes of the powerful, those in power have the privilege to escape stigmatization and consequences of illegal actions. Those in power protect their own through deciding what is illegal or not, and deciding the consequences for illegal actions. These crimes occur in private and are often underreported and under prosecuted, allowing the powerful to escape consequences. Critical analysis will address these dichotomies, challenging theoretical assumptions and criminal justice practices to advocate for structural change. Labeling Theory Background Labeling theory discusses the structural inequalities within society that explain criminality. It can be traced back to Mead’s theory of symbolic interactionism in 1934, which discusses the importance of language regarding informing social action through processes of constructing, interpreting, and transmitting meaning (Denver et al., 2017, p. 666). From there, labeling theory was further developed with Lemert’s distinction between primary and secondary deviance in 1951, which explained how deviance of an individual begins and continues (Thompson, 2014). Finally, and perhaps most influentially, we have Becker’s labeling theory of deviance in 1963, which is the version of the theory that will be guiding this discussion in the essay (Paternoster & Bachman, 2017). In Becker’s labeling theory, he describes crime as a social construct:>GET ANSWER