refactored text

2-Regression/4-Logistic/solution/R/lesson_4.Rmd

@@ -72,17 +72,6 @@ Logistic regression does not offer the same features as linear regression. The f
 
 ![Infographic by Dasani Madipalli](../../images/pumpkin-classifier.png){width="600"}
 
-#### **Other classifications**
-
-There are other types of logistic regression, including multinomial and ordinal:
-
--   **Multinomial**, which involves having more than one category - "Orange, White, and Striped".
-
--   **Ordinal**, which involves ordered categories, useful if we wanted to order our outcomes logically, like our pumpkins that are ordered by a finite number of sizes (mini,sm,med,lg,xl,xxl).
-
-![Multinomial vs ordinal regression](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/4-Logistic/images/multinomial-vs-ordinal.png)
-
-
 #### **Variables DO NOT have to correlate**
 
 Remember how linear regression worked better with more correlated variables? Logistic regression is the opposite - the variables don't have to align. That works for this data which has somewhat weak correlations.
@@ -238,11 +227,8 @@ baked_pumpkins %>%
   scale_color_brewer(palette = "Dark2", direction = -1) +
   theme(legend.position = "none")
 ```
-
-
 Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore logistic regression to determine a given pumpkin's likely color.
 
-
 ## 3. Build your model
 
 Let's begin by splitting the data into `training` and `test` sets. The training set is used to train a classifier so that it finds a statistical relationship between the features and the label value.
@@ -262,8 +248,6 @@ pumpkins_test <- testing(pumpkins_split)
 # Print out the first 5 rows of the training set
 pumpkins_train %>% 
   slice_head(n = 5)
-
-
 ```
 
 🙌 We are now ready to train a model by fitting the training features to the training label (color).
@@ -299,7 +283,6 @@ log_reg_wf <- workflow() %>%
 # Print out the workflow
 log_reg_wf
 
-
 ```
 
 After a workflow has been *specified*, a model can be `trained` using the [`fit()`](https://tidymodels.github.io/parsnip/reference/fit.html) function. The workflow will estimate a recipe and preprocess the data before training, so we won't have to manually do that using prep and bake.