`make_quads()` Produces True Positive, False Positive, True Negative, and False Negative quantities from two binary input vectors of predicted and observed classes.
make_quads(pred, obs)
| pred | - [vector] Predicted probabilities. |
|---|---|
| obs | - [vector] Observed presence/absence as 1/0 |
[vector] TP, FP, TN, FN names vector
This function takes two binary vectors coded as 1 == presence/positive and 0 == absent/negative. The `pred` vector is the predicted binary class and `obs` vector is the true observed class. The output quantites can be used for additional metric evaluation.
# NOT RUN { sim_data <- get_sim_data(site_samples = 800, N_site_bags = 75, sites_var1_mean = 80, sites_var1_sd = 10, sites_var2_mean = 5, sites_var2_sd = 2, backg_var1_mean = 100,backg_var1_sd = 20, backg_var2_mean = 6, backg_var2_sd = 3) formatted_data <- format_site_data(sim_data, N_sites=10, train_test_split=0.8, sample_fraction = 0.9, background_site_balance=1) train_data <- formatted_data[["train_data"]] train_presence <- formatted_data[["train_presence"]] test_presence <- formatted_data[["test_presence"]] ##### Logistic Mean Embedding KLR Model #### Build Kernel Matrix K <- build_K(train_data, sigma = sigma, dist_metric = dist_metric) #### Train train_log_pred <- KLR(K, train_presence, lambda, 100, 0.001, verbose = 2) #### Predict test_log_pred <- KLR_predict(test_data, train_data, dist_metric = dist_metric, train_log_pred[["alphas"]], sigma) cm <- make_quads(ifelse(test_log_pred >= 0.5, 1, 0), test_presence) # }