We find that our OLS model has an MAE of only MAE: 2.66–not bad for such a simple model! Still, it struggles most in the areas where we most need it to succeed, so we will try to introduce better variables and apply a more complex model to improve our predictions.
-
-
4.2 Random Forest Regression
+
+
4.2 Random Forest Regression: Testing
+
We train and test up to 2022–we use this for model tuning and feature engineering.
We find that error is not related to affordability and actually trends downward with percent nonwhite. (This is probably because there is less total development happening there in majority-minority neighborhoods to begin with, so the magnitude of error is less, even though proportionally it might be more.) Error increases slightly with total pop. This makes sense–more people –> more development.
Show the code
-
ggplot(rf_predictions, aes(y = abs_error, x = rent_burden)) +# or whatever the variable is
-geom_point() +
-geom_smooth(method ="lm", se=FALSE) +
-theme_minimal()
+
ggplot(rf_val_preds, aes(y = abs_error, x = rent_burden)) +# or whatever the variable is
+geom_point() +
+geom_smooth(method ="lm", se=FALSE) +
+theme_minimal()
-
+
Show the code
-
ggplot(rf_predictions, aes(y = abs_error, x = percent_nonwhite)) +# or whatever the variable is
-geom_point() +
-geom_smooth(method ="lm", se=FALSE) +
-theme_minimal()
+
ggplot(rf_val_preds, aes(y = abs_error, x = percent_nonwhite)) +# or whatever the variable is
+geom_point() +
+geom_smooth(method ="lm", se=FALSE) +
+theme_minimal()
-
+
Show the code
-
ggplot(rf_predictions, aes(y = abs_error, x = total_pop)) +# or whatever the variable is
-geom_point() +
-geom_smooth(method ="lm", se=FALSE) +
-theme_minimal()
+
ggplot(rf_val_preds, aes(y = abs_error, x = total_pop)) +# or whatever the variable is
+geom_point() +
+geom_smooth(method ="lm", se=FALSE) +
+theme_minimal()
-
+
Show the code
-
ggplot(rf_predictions, aes(y = abs_error, x = med_inc)) +# or whatever the variable is
-geom_point() +
-geom_smooth(method ="lm", se=FALSE) +
-theme_minimal()
+
ggplot(rf_val_preds, aes(y = abs_error, x = med_inc)) +# or whatever the variable is
+geom_point() +
+geom_smooth(method ="lm", se=FALSE) +
+theme_minimal()
-
+
How does this generalize across council districts? Don’t forget to refactor
Show the code
-
suppressMessages(
-ggplot(rf_predictions, aes(x =reorder(district, abs_error, FUN = mean), y = abs_error)) +
-geom_boxplot(fill =NA) +
-labs(title ="MAE by Council District",
-y ="Mean Absolute Error",
-x ="Council District") +
-theme_minimal() +
-theme()
-)
+
suppressMessages(
+ggplot(rf_val_preds, aes(x =reorder(district, abs_error, FUN = mean), y = abs_error)) +
+geom_boxplot(fill =NA) +
+labs(title ="MAE by Council District",
+y ="Mean Absolute Error",
+x ="Council District") +
+theme_minimal() +
+theme()
+)
We find that our OLS model has an MAE of only MAE: 2.66–not bad for such a simple model! Still, it struggles most in the areas where we most need it to succeed, so we will try to introduce better variables and apply a more complex model to improve our predictions.
-
-
4.2 Random Forest Regression
+
+
4.2 Random Forest Regression: Testing
+
We train and test up to 2022–we use this for model tuning and feature engineering.
We find that error is not related to affordability and actually trends downward with percent nonwhite. (This is probably because there is less total development happening there in majority-minority neighborhoods to begin with, so the magnitude of error is less, even though proportionally it might be more.) Error increases slightly with total pop. This makes sense–more people –> more development.
Show the code
-
ggplot(rf_predictions, aes(y = abs_error, x = rent_burden)) +# or whatever the variable is
-geom_point() +
-geom_smooth(method ="lm", se=FALSE) +
-theme_minimal()
+
ggplot(rf_val_preds, aes(y = abs_error, x = rent_burden)) +# or whatever the variable is
+geom_point() +
+geom_smooth(method ="lm", se=FALSE) +
+theme_minimal()
-
+
Show the code
-
ggplot(rf_predictions, aes(y = abs_error, x = percent_nonwhite)) +# or whatever the variable is
-geom_point() +
-geom_smooth(method ="lm", se=FALSE) +
-theme_minimal()
+
ggplot(rf_val_preds, aes(y = abs_error, x = percent_nonwhite)) +# or whatever the variable is
+geom_point() +
+geom_smooth(method ="lm", se=FALSE) +
+theme_minimal()
-
+
Show the code
-
ggplot(rf_predictions, aes(y = abs_error, x = total_pop)) +# or whatever the variable is
-geom_point() +
-geom_smooth(method ="lm", se=FALSE) +
-theme_minimal()
+
ggplot(rf_val_preds, aes(y = abs_error, x = total_pop)) +# or whatever the variable is
+geom_point() +
+geom_smooth(method ="lm", se=FALSE) +
+theme_minimal()
-
+
Show the code
-
ggplot(rf_predictions, aes(y = abs_error, x = med_inc)) +# or whatever the variable is
-geom_point() +
-geom_smooth(method ="lm", se=FALSE) +
-theme_minimal()
+
ggplot(rf_val_preds, aes(y = abs_error, x = med_inc)) +# or whatever the variable is
+geom_point() +
+geom_smooth(method ="lm", se=FALSE) +
+theme_minimal()
-
+
How does this generalize across council districts? Don’t forget to refactor
Show the code
-
suppressMessages(
-ggplot(rf_predictions, aes(x =reorder(district, abs_error, FUN = mean), y = abs_error)) +
-geom_boxplot(fill =NA) +
-labs(title ="MAE by Council District",
-y ="Mean Absolute Error",
-x ="Council District") +
-theme_minimal() +
-theme()
-)
+
suppressMessages(
+ggplot(rf_val_preds, aes(x =reorder(district, abs_error, FUN = mean), y = abs_error)) +
+geom_boxplot(fill =NA) +
+labs(title ="MAE by Council District",
+y ="Mean Absolute Error",
+x ="Council District") +
+theme_minimal() +
+theme()
+)
