accuracy_plot
Description
Returns a ggplot2 plot object containing an accuracy @ percentile plot
Usage
accuracy_plot(test.y, pred.prob, granularity = 0.02, show_numbers = T)accuracy_plot(test.y, pred.prob, granularity = 0.02, show_numbers = T)
Arguments
test.y |
List of know labels on the test set |
pred.prob |
List of probability predictions on the test set |
granularity |
Default 0.02, probability step between points in plot. |
show_numbers |
Show values as numbers above the plot line |
calculate_auc
Description
Compute auc from predictions and truth
Usage
calculate_auc(test.y, pred.prob)calculate_auc(test.y, pred.prob)
Arguments
test.y |
List of know labels on the test set |
pred.prob |
List of probability predictions on the test set |
Value
auc
calibration_plot
Description
Returns a ggplot2 plot object containing a smoothed propensity @ prediction level plot
Usage
calibration_plot(test.y, pred.prob)calibration_plot(test.y, pred.prob)
Arguments
test.y |
List of know labels on the test set |
pred.prob |
List of probability predictions on the test set |
The main functions you want are classifierplots or classifierplots_folder.
Description
The main functions you want are classifierplots or classifierplots_folder.
Produce a suit of classifier diagnostic plots
Usage
classifierplots(test.y, pred.prob)classifierplots(test.y, pred.prob)
Arguments
test.y |
List of know labels on the test set |
pred.prob |
List of probability predictions on the test set |
Details
Examples
## Not run: classifierplots(example_predictions$test.y, example_predictions$pred.prob) ## End(Not run)## Not run: classifierplots(example_predictions$test.y, example_predictions$pred.prob) ## End(Not run)
classifierplots_folder
Description
Produce a suit of classifier diagnostic plots, saving to disk.
Usage
classifierplots_folder(test.y, pred.prob, folder, height = 5, width = 5)classifierplots_folder(test.y, pred.prob, folder, height = 5, width = 5)
Arguments
test.y |
List of know labels on the test set |
pred.prob |
List of probability predictions on the test set |
folder |
Directory to save plots into |
height |
height of separately saved plots |
width |
width of separately saved plots |
density_plot
Description
Returns a ggplot2 plot object containing a score density plot.
Usage
density_plot(test.y, pred.prob)density_plot(test.y, pred.prob)
Arguments
test.y |
List of know labels on the test set |
pred.prob |
List of probability predictions on the test set |
Generated using the gen_example included in the github source
Description
Generated using the gen_example included in the github source
lift_plot
Description
Returns a ggplot2 plot object containing an precision @ percentile plot
Usage
lift_plot(test.y, pred.prob, granularity = 0.02, show_numbers = T)lift_plot(test.y, pred.prob, granularity = 0.02, show_numbers = T)
Arguments
test.y |
List of know labels on the test set |
pred.prob |
List of probability predictions on the test set |
granularity |
Default 0.02, probability step between points in plot. |
show_numbers |
Show numbers at deciles T/F default T. |
notation_key_plot
Description
Produces some definitions as a grid.
Usage
notation_key_plot()notation_key_plot()
positives_plot
Description
Returns a ggplot2 plot object containing an positives-per-decile plot.
Usage
positives_plot(test.y, pred.prob)positives_plot(test.y, pred.prob)
Arguments
test.y |
List of know labels on the test set |
pred.prob |
List of probability predictions on the test set |
precision_plot
Description
Returns a ggplot2 plot object containing an precision @ percentile plot
Usage
precision_plot(test.y, pred.prob, granularity = 0.02, show_numbers = T)precision_plot(test.y, pred.prob, granularity = 0.02, show_numbers = T)
Arguments
test.y |
List of know labels on the test set |
pred.prob |
List of probability predictions on the test set |
granularity |
Default 0.02, probability step between points in plot. |
show_numbers |
Show numbers at deciles T/F default T. |
propensity_plot
Description
Returns a ggplot2 plot object containing an propensity @ percentile plot
Usage
propensity_plot(test.y, pred.prob, granularity = 0.02)propensity_plot(test.y, pred.prob, granularity = 0.02)
Arguments
test.y |
List of know labels on the test set |
pred.prob |
List of probability predictions on the test set |
granularity |
Default 0.02, probability step between points in plot. |
recall_plot
Description
Returns a ggplot2 plot object containing an sensitivity @ percentile plot
Usage
recall_plot(test.y, pred.prob, granularity = 0.02, show_numbers = T)recall_plot(test.y, pred.prob, granularity = 0.02, show_numbers = T)
Arguments
test.y |
List of know labels on the test set |
pred.prob |
List of probability predictions on the test set |
granularity |
Default 0.02, probability step between points in plot. |
show_numbers |
Show numbers at deciles T/F default T. |
roc_plot
Description
Produces a smoothed ROC curve as a ggplot2 plot object. A confidence interval is produced using bootstrapping, although it is turned off by default if you have a large dataset.
Usage
roc_plot(test.y, pred.prob, resamps = 2000, force_bootstrap = NULL)roc_plot(test.y, pred.prob, resamps = 2000, force_bootstrap = NULL)
Arguments
test.y |
List of know labels on the test set |
pred.prob |
List of probability predictions on the test set |
resamps |
How many bootstrap samples to use |
force_bootstrap |
True/False to force or force off bootstrapping. |
sigmoid
Description
Logistic sigmoid function, that maps any real number to the [0,1] interval. Supports vectors of numeric.
Usage
sigmoid(x)sigmoid(x)
Arguments
x |
data |