10/31/22, 1:34 PM Role Delineation
https://barry.instructure.com/courses/1612798/assignments/7073264 1/1
Total Points: 100
Role Delineation
Due No Due Date Points 100 Submitting a file upload (Turnitin enabled)
Role Delineation
Criteria Ratings Pts
25 pts
20 pts
20 pts
20 pts
5 pts
5 pts
5 pts
Start Assignment
The role delineation section allows the reader to identify how you will actualize each competency outlined by the National Organization of Nurse Practitioner Faculty (NONPF) and the AACN. It is as unique as each student. You may write a narrative and include supporting documents to highlight your activities. Ultimately it shows the progression towards the program goals.
Essential Components of Role Delineation
Examples /Supporting Evidence
Role Delineation based on NP Competencies
Narrative format plus evidence is required
Management of Patient Health/Illness Status
Reflections on class lectures, discussions, clinical experiences, readings, assignments
NP-Pt Relationship Reflections on class lectures, discussions, clinical experiences readings, assignments
Teaching-Coaching Function
Sample of outline of teaching rounds, poster, in- service etc
Professional Role May be evident in C-V, ,Proof of attendance at BON meeting
Negotiating HC Delivery Systems
Attendance at Lambda Chi Meeting, Pri-Med, CMS seminar
Monitoring/ Ensuring Quality of HC
Sample of peer reviews, quality improvement activities, Magnet offerings
Cultural Competence Article citation, Web References
Managment of Patient Health/Illness Status 25 pts Full Marks
0 pts No Marks
NP-PT Relationship 20 pts Full Marks
0 pts No Marks
Teaching-Coaching Function 20 pts Full Marks
0 pts No Marks
Professional Role 20 pts Full Marks
0 pts No Marks
Negotiating HC Delivery Systems 5 pts Full Marks
0 pts No Marks
Monitoring/ Ensuring Quality of HC 5 pts Full Marks
0 pts No Marks
Cultural Competence 5 pts Full Marks
0 pts No Marks
classification
March 24, 2022
[1]: import numpy as np from sklearn.naive_bayes import GaussianNB
[2]: X = np.genfromtxt('iris.csv', delimiter=',', skip_header=0)
[3]: X.shape
[3]: (150, 5)
[4]: # Shuffle X so that the classes are more uniformly distributed. np.random.seed(12) # having the same seed allows to always generate the same␣ ↪→pseudorandom numbers.
np.random.shuffle(X)
[5]: # Divide X into a training set (100 rows) and a test set (50 rows). X_train = X[:100, :4] # training set, features y_train = X[:100, 4] # training set, targets X_test = X[100:, :4] # test set, features y_test = X[100:, 4] # test set, targets
[6]: # The training set is used to train a model that will classify new examples. # The function fit takes two parameters: the features ( or attributes) and the␣ ↪→target.
model = GaussianNB().fit(X_train, y_train)
[7]: # Apply new test cases to the model, and store the predictions in a variable␣ ↪→y_pred.
y_pred = model.predict(X_test)
[8]: # Find the accuracy of the model # the accuracy of the model is found by counting the number of correct␣ ↪→predictions.
[9]: np.sum(y_pred == y_test)
[9]: 47
1
[10]: # accuracy in percentage np.sum(y_pred == y_test) / len(y_test) * 100
[10]: 94.0
[ ]:
2
(a) Read the notes on classification.
(b) Modify the classification code to run the same analysis but on the data set titanic.csv Download titanic.csv .
Some information on the data set: - data on the Titanic disaster - The goal is to predict if a given person survived or not the Titanic disaster. - each row represents one person (passenger) on the Titanic - target: survived (0), did not survive (1). This is the first column. - features: Pclass, Sex, Age, Siblings/Spouses Aboard, Parents/Children Aboard, Fare
Use the same classification algorithm GaussianNB, and split the data set into 700 rows for training, and the rest for testing. Use a random seed equal to 12.
(c) Repeat the previous step, but use the following classification algorithms. You can learn how to use these classifiers on the scikit-learn website.
KNeighborsClassifier RandomForestClassifier ExtraTreesClassifier SVC
(d) Upload your entire code to Canvas in a Python file, i.e. the extension of the file should be py.

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