remove unnecessary set_mode() calls

Author: topepo

man/contr_one_hot.Rd

@@ -10,7 +10,6 @@ Engines may have pre-set default arguments when executing the model fit call. Fo
 ```{r lm-reg}
 linear_reg() %>% 
   set_engine("lm") %>% 
-  set_mode("regression") %>% 
   translate()
 ```
 
@@ -19,7 +18,6 @@ linear_reg() %>%
 ```{r glmnet-csl}
 linear_reg(penalty = 0.1) %>% 
   set_engine("glmnet") %>% 
-  set_mode("regression") %>% 
   translate()
 ```
 
@@ -37,7 +35,6 @@ penalty results.
 ```{r stan-reg}
 linear_reg() %>% 
   set_engine("stan") %>% 
-  set_mode("regression") %>% 
   translate()
 ```
 
@@ -55,7 +52,6 @@ returned.
 ```{r spark-reg}
 linear_reg() %>% 
   set_engine("spark") %>% 
-  set_mode("regression") %>% 
   translate()
 ```
 
@@ -64,7 +60,6 @@ linear_reg() %>%
 ```{r keras-reg}
 linear_reg() %>% 
   set_engine("keras") %>% 
-  set_mode("regression") %>% 
   translate()
 ```
 

man/linear_reg.Rd

@@ -11,7 +11,6 @@ For this type of model, the template of the fit calls are below.
 ```{r glm-reg}
 logistic_reg() %>% 
   set_engine("glm") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -20,7 +19,6 @@ logistic_reg() %>%
 ```{r glmnet-csl}
 logistic_reg(penalty = 0.1) %>% 
   set_engine("glmnet") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -38,7 +36,6 @@ penalty results.
 ```{r liblinear-reg}
 logistic_reg() %>% 
   set_engine("LiblineaR") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -54,7 +51,6 @@ regularized regression models do not, which will result in different parameter e
 ```{r stan-reg}
 logistic_reg() %>% 
   set_engine("stan") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -72,7 +68,6 @@ returned.
 ```{r spark-reg}
 logistic_reg() %>% 
   set_engine("spark") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -81,7 +76,6 @@ logistic_reg() %>%
 ```{r keras-reg}
 logistic_reg() %>% 
   set_engine("keras") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 

man/logistic_reg.Rd

@@ -11,7 +11,6 @@ For this type of model, the template of the fit calls are below.
 ```{r glmnet-cls}
 multinom_reg(penalty = 0.1) %>% 
   set_engine("glmnet") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -29,7 +28,6 @@ penalty results.
 ```{r nnet-cls}
 multinom_reg() %>% 
   set_engine("nnet") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -38,7 +36,6 @@ multinom_reg() %>%
 ```{r spark-cls}
 multinom_reg() %>% 
   set_engine("spark") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -47,7 +44,6 @@ multinom_reg() %>%
 ```{r keras-cls}
 multinom_reg() %>% 
   set_engine("keras") %>% 
-  set_mode("classification") %>% 
   translate()
 ```