@ -150,6 +150,12 @@ The goal of data exploration is to try to understand the `relationships` between
Given our the data types of our columns, we can `encode` them and be on our way to making some visualizations. This simply involves `translating` a column with `categorical values` for example our columns of type *char*, into one or more `numeric columns` that take the place of the original. - Something we did in our [last lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/solution/lesson_3.html).
Given our the data types of our columns, we can `encode` them and be on our way to making some visualizations. This simply involves `translating` a column with `categorical values` for example our columns of type *char*, into one or more `numeric columns` that take the place of the original. - Something we did in our [last lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/solution/lesson_3.html).
For feature encoding there are two main types of encoders:
1. Ordinal encoder: it suits well for ordinal variables, which are categorical variables where their data follows a logical ordering, like the `item_size` column in our dataset. It creates a mapping such that each category is represented by a number, which is the order of the category in the column.
2. Categorical encoder: it suits well for nominal variables, which are categorical variables where their data does not follow a logical ordering, like all the features different from `item_size` in our dataset. It is a one-hot encoding, which means that each category is represented by a binary column: the encoded variable is equal to 1 if the pumpkin belongs to that Variety and 0 otherwise.
Tidymodels provides yet another neat package: [recipes](https://recipes.tidymodels.org/)- a package for preprocessing data. We'll define a `recipe` that specifies that all predictor columns should be encoded into a set of integers , `prep` it to estimates the required quantities and statistics needed by any operations and finally `bake` to apply the computations to new data.
Tidymodels provides yet another neat package: [recipes](https://recipes.tidymodels.org/)- a package for preprocessing data. We'll define a `recipe` that specifies that all predictor columns should be encoded into a set of integers , `prep` it to estimates the required quantities and statistics needed by any operations and finally `bake` to apply the computations to new data.
> Normally, recipes is usually used as a preprocessor for modelling where it defines what steps should be applied to a data set in order to get it ready for modelling. In that case it is **highly recommend** that you use a `workflow()` instead of manually estimating a recipe using prep and bake. We'll see all this in just a moment.
> Normally, recipes is usually used as a preprocessor for modelling where it defines what steps should be applied to a data set in order to get it ready for modelling. In that case it is **highly recommend** that you use a `workflow()` instead of manually estimating a recipe using prep and bake. We'll see all this in just a moment.
Now let's compare the feature distributions for each label value using box plots. We'll begin by formatting the data to a *long* format to make it somewhat easier to make multiple `facets`.
Now let's compare the feature distributions for each label value using box plots. We'll begin by formatting the data to a *long* format to make it somewhat easier to make multiple `facets`.
@ -255,22 +263,22 @@ pumpkins_train %>%
🙌 We are now ready to train a model by fitting the training features to the training label (color).
🙌 We are now ready to train a model by fitting the training features to the training label (color).
We'll begin by creating a recipe that specifies the preprocessing steps that should be carried out on our data to get it ready for modelling i.e: encoding categorical variables into a set of integers.
We'll begin by creating a recipe that specifies the preprocessing steps that should be carried out on our data to get it ready for modelling i.e: encoding categorical variables into a set of integers. Just like `baked_pumpkins`, we create a `pumpkins_recipe` but do not `prep` and `bake` since it would be bundled into a workflow, which you will see in just a few steps from now.
There are quite a number of ways to specify a logistic regression model in Tidymodels. See `?logistic_reg()` For now, we'll specify a logistic regression model via the default `stats::glm()` engine.
There are quite a number of ways to specify a logistic regression model in Tidymodels. See `?logistic_reg()` For now, we'll specify a logistic regression model via the default `stats::glm()` engine.
```{r log_reg}
```{r log_reg}
# Create a recipe that specifies preprocessing steps for modelling
# Create a recipe that specifies preprocessing steps for modelling
pumpkins_recipe <- recipe(color ~ ., data = pumpkins_train) %>%
pumpkins_recipe <- recipe(color ~ ., data = pumpkins_train) %>%
Now that we have a recipe and a model specification, we need to find a way of bundling them together into an object that will first preprocess the data (prep+bake behind the scenes), fit the model on the preprocessed data and also allow for potential post-processing activities.
Now that we have a recipe and a model specification, we need to find a way of bundling them together into an object that will first preprocess the data (prep+bake behind the scenes), fit the model on the preprocessed data and also allow for potential post-processing activities.