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DataCamp Supervised Learning in R: Case Studies
Predicting voter turnout from survey data
SUPERVISED LEARNING IN R: CASE STUDIES
DataCamp Supervised Learning in R: Case Studies
Predicting voter turnout from survey data Julia Silge Data - - PowerPoint PPT Presentation
Predicting voter turnout from survey data Julia Silge Data - - PowerPoint PPT Presentation
DataCamp Supervised Learning in R: Case Studies SUPERVISED LEARNING IN R : CASE STUDIES Predicting voter turnout from survey data Julia Silge Data Scientist at Stack Overflow DataCamp Supervised Learning in R: Case Studies Views of the
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DataCamp Supervised Learning in R: Case Studies
Views of the Electorate Research Survey (VOTER)
Life in America today for people like you compared to fifty years ago is better? about the same? worse? Was your vote primarily a vote in favor of your choice or was it mostly a vote against his/her opponent? How important are the following issues to you? Crime Immigration The environment Gay rights
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DataCamp Supervised Learning in R: Case Studies
Views of the Electorate Research Survey (VOTER)
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DataCamp Supervised Learning in R: Case Studies
Interpreting integer survey responses
AMERICA IS A FAIR SOCIETY WHERE EVERYONE HAS THE OPPORTUNITY TO GET AHEAD
Response Code Strongly agree 1 Agree 2 Disagree 3 Strongly disagree 4
Learn ! more about the data yourself
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DataCamp Supervised Learning in R: Case Studies
Predicting voter turnout
> voters %>% + count(turnout16_2016) # A tibble: 2 x 2 turnout16_2016 n <fct> <int> 1 Did not vote 264 2 Voted 6428
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DataCamp Supervised Learning in R: Case Studies
Let's get started!
SUPERVISED LEARNING IN R: CASE STUDIES
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DataCamp Supervised Learning in R: Case Studies
VOTE 2016
SUPERVISED LEARNING IN R: CASE STUDIES
Julia Silge
Data Scientist at Stack Overflow
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DataCamp Supervised Learning in R: Case Studies
Exploratory data analysis
Elections don't matter Gay rights are very important Crime is very important Did not vote 55.3% 17.0% 66.3% Voted 34.1% 25.3% 57.6%
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DataCamp Supervised Learning in R: Case Studies
Exploratory data analysis
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DataCamp Supervised Learning in R: Case Studies
Fitting a simple model
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DataCamp Supervised Learning in R: Case Studies
Fitting a simple model
> library(broom) > > simple_glm %>% + tidy() %>% + filter(p.value < 0.05) %>% + arrange(desc(estimate)) term estimate std.error statistic p.value 1 (Intercept) 2.45703562 0.73272138 3.353301 7.985370e-04 2 imiss_a_2016 0.39712084 0.13898678 2.857256 4.273207e-03 3 imiss_l_2016 0.27468893 0.10678119 2.572447 1.009825e-02 4 imiss_q_2016 0.24456695 0.11909335 2.053573 4.001699e-02 5 track_2016 0.24107452 0.12146679 1.984695 4.717843e-02 6 RIGGED_SYSTEM_1_2016 0.23628350 0.08508091 2.777162 5.483579e-03 7 futuretrend_2016 0.21056782 0.07120079 2.957380 3.102651e-03 8 RIGGED_SYSTEM_5_2016 0.19025188 0.09645384 1.972466 4.855648e-02 9 wealth_2016 -0.06940523 0.02634395 -2.634580 8.424157e-03 10 imiss_k_2016 -0.18103020 0.08272555 -2.188323 2.864611e-02 11 econtrend_2016 -0.29536980 0.08722417 -3.386330 7.083422e-04 12 imiss_f_2016 -0.32328040 0.10543220 -3.066240 2.167694e-03 13 imiss_g_2016 -0.33203385 0.07867346 -4.220405 2.438640e-05 14 imiss_n_2016 -0.44161183 0.09003981 -4.904628 9.360434e-07
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DataCamp Supervised Learning in R: Case Studies
Let's build some models!
SUPERVISED LEARNING IN R: CASE STUDIES
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DataCamp Supervised Learning in R: Case Studies
Cross-validation
SUPERVISED LEARNING IN R: CASE STUDIES
Julia Silge
Data Scientist at Stack Overflow
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DataCamp Supervised Learning in R: Case Studies
Cross-validation
Partitioning your data into subsets and using one subset for validation
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DataCamp Supervised Learning in R: Case Studies
Cross-validation
Partitioning your data into subsets and using one subset for validation
method = "cv" method = "repeatedcv"
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DataCamp Supervised Learning in R: Case Studies
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DataCamp Supervised Learning in R: Case Studies
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DataCamp Supervised Learning in R: Case Studies
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DataCamp Supervised Learning in R: Case Studies
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DataCamp Supervised Learning in R: Case Studies
Cross-validation
Repeated cross-validation can take a long time Parallel processing can be worth it
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DataCamp Supervised Learning in R: Case Studies
Let's practice!
SUPERVISED LEARNING IN R: CASE STUDIES
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DataCamp Supervised Learning in R: Case Studies
Comparing model performance
SUPERVISED LEARNING IN R: CASE STUDIES
Julia Silge
Data Scientist at Stack Overflow
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DataCamp Supervised Learning in R: Case Studies
Confusion matrix
> confusionMatrix(predict(fit_glm, training), + training$turnout16_2016) Confusion Matrix and Statistics Reference Prediction Did not vote Voted Did not vote 149 1633 Voted 63 3510 Accuracy : 0.6833 95% CI : (0.6706, 0.6957) No Information Rate : 0.9604 P-Value [Acc > NIR] : 1 Kappa : 0.0847 Mcnemar's Test P-Value : <2e-16 Sensitivity : 0.70283 Specificity : 0.68248 Pos Pred Value : 0.08361 Neg Pred Value : 0.98237 Prevalence : 0.03959 Detection Rate : 0.02782 Detection Prevalence : 0.33277 Balanced Accuracy : 0.69266 'P iti ' Cl Did t t
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DataCamp Supervised Learning in R: Case Studies
Confusion matrix
> confusionMatrix(predict(fit_rf, training), + training$turnout16_2016) Confusion Matrix and Statistics Reference Prediction Did not vote Voted Did not vote 212 5 Voted 0 5138 Accuracy : 0.9991 95% CI : (0.9978, 0.9997) No Information Rate : 0.9604 P-Value [Acc > NIR] : < 2e-16 Kappa : 0.9879 Mcnemar's Test P-Value : 0.07364 Sensitivity : 1.00000 Specificity : 0.99903 Pos Pred Value : 0.97696 Neg Pred Value : 1.00000 Prevalence : 0.03959 Detection Rate : 0.03959 Detection Prevalence : 0.04052 Balanced Accuracy : 0.99951 'P iti ' Cl Did t t
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DataCamp Supervised Learning in R: Case Studies
Confusion matrix for the testing data
> confusionMatrix(predict(fit_glm, testing), + testing$turnout16_2016) Confusion Matrix and Statistics Reference Prediction Did not vote Voted Did not vote 37 428 Voted 15 857 Accuracy : 0.6687 95% CI : (0.6427, 0.6939) No Information Rate : 0.9611 P-Value [Acc > NIR] : 1 Kappa : 0.0787 Mcnemar's Test P-Value : <2e-16 Sensitivity : 0.71154 Specificity : 0.66693 Pos Pred Value : 0.07957 Neg Pred Value : 0.98280 Prevalence : 0.03889 Detection Rate : 0.02767 Detection Prevalence : 0.34779 Balanced Accuracy : 0.68923 'P iti ' Cl Did t t
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DataCamp Supervised Learning in R: Case Studies
Confusion matrix for the testing data
> confusionMatrix(predict(fit_rf, testing), + testing$turnout16_2016) Confusion Matrix and Statistics Reference Prediction Did not vote Voted Did not vote 0 14 Voted 52 1271 Accuracy : 0.9506 95% CI : (0.9376, 0.9616) No Information Rate : 0.9611 P-Value [Acc > NIR] : 0.9767 Kappa : -0.0168 Mcnemar's Test P-Value : 5.254e-06 Sensitivity : 0.00000 Specificity : 0.98911 Pos Pred Value : 0.00000 Neg Pred Value : 0.96070 Prevalence : 0.03889 Detection Rate : 0.00000 Detection Prevalence : 0.01047 Balanced Accuracy : 0.49455 'P iti ' Cl Did t t
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DataCamp Supervised Learning in R: Case Studies
Comparing model performance
> library(yardstick) > > sens(testing_results, truth = turnout16_2016, estimate = `Logistic regression` [1] 0.7115385 > > spec(testing_results, truth = turnout16_2016, estimate = `Logistic regression` [1] 0.6669261 > > sens(testing_results, truth = turnout16_2016, estimate = `Random forest`) [1] 0 > > spec(testing_results, truth = turnout16_2016, estimate = `Random forest`) [1] 0.9891051
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DataCamp Supervised Learning in R: Case Studies
Let's finish this case study!
SUPERVISED LEARNING IN R: CASE STUDIES