diff --git a/NEWS.md b/NEWS.md
index d261acf..2ce4d31 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,7 @@
 # tidypredict 0.4.9
 
+- Fixes issue handling GLM Binomial earth models (#97)
+
 - Adds capability to handle single simple Cubist models (#57)
 
 - Fixed parenthesis issue in the creation of the interval formula (#76)
diff --git a/R/model-earth.R b/R/model-earth.R
index 930f13a..b8ec292 100644
--- a/R/model-earth.R
+++ b/R/model-earth.R
@@ -14,10 +14,7 @@ parse_model.earth <- function(model) {
   }
 
   is_glm <- !is.null(model$glm.list)
-
-  all_coefs <- model$coefficients
-  if (is_glm) all_coefs <- model$glm.coefficients
-
+  
   pm <- list()
   pm$general$model <- "earth"
   pm$general$type <- "tree"
@@ -29,12 +26,12 @@ parse_model.earth <- function(model) {
     pm$general$family <- fam$family
     pm$general$link <- fam$link
   }
-  pm$terms <- mars_terms(model)
+  pm$terms <- mars_terms(model, is_glm)
   as_parsed_model(pm)
 }
 
 
-mars_terms <- function(mod) { 
+mars_terms <- function(mod, is_glm) { 
   feature_types <- 
     tibble::as_tibble(mod$dirs, rownames = "feature") %>% 
     dplyr::mutate(feature_num = dplyr::row_number()) %>% 
@@ -48,9 +45,16 @@ mars_terms <- function(mod) {
     tidyr::pivot_longer(cols = c(-feature, -feature_num),
                         values_to = "value",
                         names_to = "term")
+  
+  if (is_glm)  {
+    all_coefs <- mod$glm.coefficients
+  } else {
+    all_coefs <- mod$coefficients  
+  }
+
   feature_coefs <- 
     # Note coef(mod) formats data differently for logistic regression
-    tibble::as_tibble(mod$coefficients, rownames = "feature") %>% 
+    tibble::as_tibble(all_coefs, rownames = "feature") %>% 
     setNames(c("feature", "coefficient"))
   
   term_to_column <- 
diff --git a/R/test-predictions.R b/R/test-predictions.R
index 3fb21d6..8d198d5 100644
--- a/R/test-predictions.R
+++ b/R/test-predictions.R
@@ -68,12 +68,12 @@ tidypredict_test_default <- function(model, df = model$model, threshold = 0.0000
 
   if (is.numeric(max_rows)) df <- head(df, max_rows)
 
-  base <- predict(model, df, interval = interval, type = "response")
+  preds <- predict(model, df, interval = interval, type = "response")
 
   if (!include_intervals) {
-    base <- data.frame(fit = base, row.names = NULL)
+    base <- data.frame(fit = as.vector(preds), row.names = NULL)
   } else {
-    base <- as.data.frame(base)
+    base <- as.data.frame(preds)
   }
 
   te <- tidypredict_to_column(