In rebordao/modellatoR: Automatic deployment of a statistical model; from data to reporting.

Classification Task

Model Parameters

load('../config/params.RData')

Project Name: r params$project_name
Model: r params$method_name
Output Variable: r params$out_var
Model Formula: r params$model_formula
Type of Task: Regression r params$regression - Classification r params$classification
Timestamp: r params$timestamp
Model Parameters:

print(params$rf)

Performance Metrics

Add a graph of the trade off between bias and variance, plot the classification training test.

# Gets performance object
pred <- ROCR::prediction(predictions = output$y_est,
                         labels = output$y)

# Computes contingency table, precision and recall
nr_tp <- sum(output$y_est[which(output$y == params$pos_class)] == params$pos_class)
nr_fp <- sum(output$y_est[which(output$y != params$pos_class)] == params$pos_class)
nr_tn <- sum(output$y_est[which(output$y != params$pos_class)] != params$pos_class)
nr_fn <- sum(output$y_est[which(output$y == params$pos_class)] != params$pos_class)

# Contingency table
data.frame(pos = c(nr_tp, nr_fp), 
           neg = c(nr_fn, nr_tn), 
           row.names = c("pos", "neg"))

# Precision and recall
precision <- nr_tp / (nr_tp + nr_fp)
recall <- nr_tp / (nr_tp + nr_fn)

cat(paste0("Precision: ", precision, "\n", "Recall: ", recall))

# Displays Precision, F1-score and Recall
cols <- c('green', 'red', 'blue')
plot(performance(pred, "prec"),
     col = cols[1], ylim = c(0,1), ylab = 'Proportions', xlab = 'Cutoff',
     main = "Classification Metrics")
plot(performance(pred, "f"), col = cols[2], add = T)
plot(performance(pred, "rec"), col = cols[3], add = T)
legend('topleft', c("Precision", "F1-score","Recall"), col = cols, lwd = 2, cex = .75)

# Displays tp, fp, tn, fn
cols <- c(cols, 'magenta')
plot(performance(pred, "tpr"), col = cols[1], ylim = c(0,1),
     ylab = 'Proportions', xlab = 'Cutoff')
plot(performance(pred, "fpr"), col = cols[2], add = T)
plot(performance(pred, "tnr"), col = cols[3], add = T)
plot(performance(pred, "fnr"), col = cols[4], add = T)
legend('topleft',
c("True Positives","False Positives", "True Negatives", "False Negatives"), 
col = cols, lwd = 2, cex = .75)

# Displays tp Vs fp (ROC Curve)
plot(performance(pred, "fpr", "tpr"),
     col = cols[3], main = 'Roc Curve')

# Displays lift curve
plot(performance(pred, "lift"), col = cols[4], main = 'Lift Curve')

# Contribution of each variable towards the output
if (params$method_id == 'rf') {
 varImpPlot(model, type = 1)
varImpPlot(model, type = 2) 
}

# Shows numeric results
print(data.table(output), nrows = Inf)

rebordao/modellatoR documentation built on May 27, 2019, 4:01 a.m.