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Machine Learning for Health Metricians

Week 3: Basic Algorithms





In [ ]:



    


import numpy as np, pandas as pd, matplotlib.pyplot as plt, seaborn as sns
%matplotlib inline
sns.set_context('poster')
sns.set_style('darkgrid')

Any Questions?

Week 3

  • Just one class: review homework, discuss the key points from the reading, begin exercise of applying basic algorithms with scikits-learn
  • Before this week:
    • Read 2 chapters (DM 4, ISL 3)
    • Complete Predictive Accuracy computational exercise
  • During this week’s classes:
    • Hands-on application to DHS data.
  • Outside of classes:
    • Read 2 chapters of course text (DM 5, ISL 5)
    • Complete “Prediction” computational exercise

Lecture 3 Outline:

  • Homework Solutions
  • Cell phone ownership in DHS
  • Basic algorithms: Naïve Bayes, Linear Models, Decision Trees
  • Exercise 3: Prediction

Any (more) questions?

Anticipated questions: something about difference between statistics and machine learning, since the whole ISL chapter for this week is very familiar material on linear regression, while the DM chapter is a wild ride through a range of methods, from naive bayes to logistic regression to recursive partitioning/decision trees.

Homework Solutions

  • Searching for best $k$ value in $k$-NN

I have a set of solutions that I can circulate, but I think I will see if anyone in class is so happy with their solution that they want to share it. It should be easy to get it in front of everyone with the Sage cloud.

An aside on Open Source

  • What is Open Source Software?
  • What is GitHub?

  • What is Git?

  • Who wants more?

  • Who wants in?

Now back to our regularly scheduled program

Lecture 3 Outline:

  • Homework Solutions
  • Cell phone ownership in DHS
  • Basic algorithms: Naïve Bayes, Linear Models, Decision Trees
  • Exercise 3: Prediction

DHS Data





In [17]:



    


df = pd.read_csv('RWA_DHS6_2010_2011_HH_ASSETS.CSV', index_col=0)
df.head()



    


















    
Out[17]:










  
    

      
      sh110g
      hv247
      hv246
      hv227
      sh118f
      hv243a
      hv243c
      hv243b
      hv225
      hv209
      hv208
      hv243d
      hv207
      hv206
      hv212
      hv221
      hv210
      hv211
    

  
  
    

      0
       0
       1
       0
       1
       0
       1
       0
       1
       0
       0
       1
       0
       1
       1
       0
       0
       0
       0
    

    

      1
       0
       1
       0
       0
       0
       1
       0
       0
       1
       0
       0
       0
       1
       1
       0
       0
       0
       0
    

    

      2
       0
       1
       0
       0
       0
       1
       0
       1
       1
       0
       0
       0
       1
       0
       0
       0
       0
       1
    

    

      3
       0
       0
       0
       1
       0
       0
       0
       0
       1
       0
       0
       0
       0
       0
       0
       0
       0
       0
    

    

      4
       0
       0
       1
       1
       0
       0
       0
       0
       1
       0
       0
       0
       0
       0
       0
       0
       0
       0
    

  




















In [18]:



    


cb = pd.read_csv('RWA_DHS6_2010_2011_HH_ASSETS_codebook.CSV', index_col=0)
cb



    


















    
Out[18]:










  
    

      
      full name
    

  
  
    

      hv206
                              has electricity
    

    

      hv207
                                    has radio
    

    

      hv208
                               has television
    

    

      hv209
                             has refrigerator
    

    

      hv210
                                  has bicycle
    

    

      hv211
                       has motorcycle/scooter
    

    

      hv212
                                has car/truck
    

    

      hv221
                     has telephone (landline)
    

    

      hv225
           share toilet with other households
    

    

      hv227
            has mosquito bed net for sleeping
    

    

      hv243a
                         has mobile telephone
    

    

      hv243b
                                    has watch
    

    

      hv243c
                        has animal-drawn cart
    

    

      hv243d
                        has boat with a motor
    

    

      hv246
       owns livestock, herds, or farm animals
    

    

      hv247
                             has bank account
    

    

      sh110g
                                     computer
    

    

      sh118f
                           boat without motor

Lecture 3 Outline:

  • Homework Solutions
  • Cell phone ownership in DHS
  • Basic algorithms: Naïve Bayes, Linear Models, Decision Trees
  • Exercise 3: Prediction

Basic Algorithms

Naïve Bayes





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import sklearn.naive_bayes
clf = sklearn.naive_bayes.BernoulliNB()

Linear Models





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import sklearn.linear_model
clf = sklearn.linear_model.LinearRegression()

Decision Trees





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import sklearn.tree
clf = sklearn.tree.DecisionTreeClassifier()

Thought Experiment

  • Which do you expect to work best for predicting cell phone ownership?

Now see if you agree with your neighbors, and try to convince each other, if not.

Lecture 3 Outline:

  • Homework Solutions
  • Cell phone ownership in DHS
  • Basic algorithms: Naïve Bayes, Linear Models, Decision Trees
  • Exercise 3: Prediction

Homework:

  • Complete the many ways of predicting cell phone ownership exercise
  • Read




In [16]:



    


import ipynb_style
reload(ipynb_style)
ipynb_style.presentation()



    


















    
Out[16]:

Content source: aflaxman/AI4HM

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