Model validation

Experimental Design in R

Joanne Xiong

Data Scientist

Pre-modeling EDA

Mean and variance of outcome by variable of interest

lendingclub %>% summarise(median(loan_amnt),
                          mean(int_rate), 
                          mean(annual_inc))

lendingclub %>% group_by(verification_status) %>%
  summarise(mean(funded_amnt), 
            var(funded_amnt))

# A tibble: 3 x 3
verification_status `mean(funded_amnt)` `var(funded_amnt)`
<chr>                             <dbl>              <dbl>
1 Not Verified                   114.15          349.41953
2 Source Verified                156.14          723.53265
3 Verified                       166.08          848.54561

Pre-modeling EDA continued

Boxplot of outcome (y-axis) by variable of interest (x-axis).

ggplot(data = lendingclub,
       aes(x = verification_status, y = funded_amnt)) +
    geom_boxplot()

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Post-modeling model validation

Residual plot
QQ-plot for normality
Test ANOVA assumptions
- Homogeneity of variances
Try non-parametric alternatives to ANOVA

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Let's practice!

Experimental Design in R