```{r 07_setup, include=FALSE}
knitr::opts_chunk$set(echo=TRUE, eval=FALSE)
```

# (PART) Regression: Building Flexible Models {-}

# KNN Regression and the Bias-Variance Tradeoff

## Learning Goals {-}

- Clearly describe / implement by hand the KNN algorithm for making a regression prediction
- Explain how the number of neighbors relates to the bias-variance tradeoff
- Explain the difference between parametric and nonparametric methods
- Explain how the curse of dimensionality relates to the performance of KNN

<br>

Slides from today are available [here](https://docs.google.com/presentation/d/1XyVFAy5t0jb-hDi6gXmx3z1_WTAqe_F4rYOskSVvoLo/edit?usp=sharing).



<br><br><br>



## KNN models in `tidymodels` {-}

To build KNN models in `tidymodels`, first load the package and set the seed for the random number generator to ensure reproducible results:

```{r}
library(dplyr)
library(readr)
library(broom)
library(ggplot2)
library(tidymodels) 
tidymodels_prefer() # Resolves conflicts, prefers tidymodel functions
set.seed(___) # Pick your favorite number to fill in the parentheses
```

Then adapt the following code:

```{r}
# CV Folds
data_cv10 <- vfold_cv(___, v = 10)

# Model Specification
knn_spec <- 
  nearest_neighbor() %>% # new type of model!
  set_args(neighbors = tune()) %>% # tuning parameter is neighbor; tuning spec
  set_engine(engine = 'kknn') %>% # new engine
  set_mode('regression') 

# Recipe with standardization (!)
data_rec <- recipe( ___ ~ ___ , data = ___) %>%
    step_nzv(all_predictors()) %>% # removes variables with the same value
    step_novel(all_nominal_predictors()) %>% # important if you have rare categorical variables 
    step_normalize(all_numeric_predictors()) %>%  # important standardization step for KNN
    step_dummy(all_nominal_predictors())  # creates indicator variables for categorical variables (important for KNN!)

# Workflow (Recipe + Model)
knn_wf <- workflow() %>%
  add_model(knn_spec) %>% 
  add_recipe(data_rec)

# Tune model trying a variety of values for neighbors (using 10-fold CV)
penalty_grid <- grid_regular(
  neighbors(range = c(1, 50)), #  min and max of values for neighbors
  levels = 50) # number of neighbors values

knn_fit_cv <- tune_grid(knn_wf, # workflow
              resamples = data_cv10, #CV folds
              grid = penalty_grid, # grid specified above
              metrics = metric_set(rmse, mae))


```


<br>

Note:  `tidymodels` defines neighbors as the cases that are the closest in terms of the Euclidean distance of the predictor values.

$$
d(case_i,case_j) = \sqrt{(x_{i1} - x_{j1})^2 + \cdots +(x_{ip} - x_{jp})^2 }
$$


<br>

**Identifying the "best" KNN model**

The "best" model in the sequence of models fit is defined relative to the chosen `metric` and the choice of `select_best` or `select_by_one_std_err()`.

```{r}
knn_fit_cv %>% autoplot() # Visualize Trained Model using CV

knn_fit_cv %>% show_best(metric = 'mae') # Show evaluation metrics for different values of neighbors, ordered

# Choose value of Tuning Parameter (neighbors)
tuned_knn_wf <- knn_fit_cv %>% 
  select_by_one_std_err(metric = 'mae',desc(neighbors)) %>%  # Choose neighbors value that leads to the highest neighbors within 1 se of the lowest CV MAE
  finalize_workflow(knn_wf, .)

# Fit final KNN model to data
knn_fit_final <- tuned_knn_wf %>%
  fit(data = __) 

# Use the best model to make predictions
# new_data should be a data.frame with required predictors
predict(knn_fit_final, new_data = ___)
```



<br><br><br>



## Exercises {-}

**You can download a template RMarkdown file to start from [here](template_rmds/07-knn-bvt.Rmd).**

We'll explore KNN regression using the `College` dataset in the `ISLR` package (install it with `install.packages("ISLR")` in the Console). You can use `?College` in the Console to look at the data codebook.

```{r}
library(ISLR)
library(dplyr)
library(readr)
library(broom)
library(ggplot2)
library(tidymodels) 
tidymodels_prefer() # Resolves conflicts, prefers tidymodel functions


data(College)

# data cleaning
college_clean <- College %>% 
    mutate(school = rownames(College)) %>% # creates variable with school name
    filter(Grad.Rate <= 100) # Remove one school with grad rate of 118%
rownames(college_clean) <- NULL # Remove school names as row names
```


### Hello, how are things? {-}

Spend a few minutes chatting about life - how are you feeling? What's on your mind?


### Exercise 1: Bias-variance tradeoff warmup {-}

a. Think back to the LASSO algorithm which depends upon tuning parameter $\lambda$.
    - For which values of $\lambda$ (small or large) will LASSO be the most biased, and why?
    - For which values of $\lambda$ (small or large) will LASSO be the most variable, and why?

b. The bias-variance tradeoff also comes into play when comparing across algorithms, not just within algorithms. Consider LASSO vs. least squares:
    - Which will tend to be more biased?
    - Which will tend to be more variable?
    - When will LASSO outperform least squares in the bias-variance tradeoff?


### Exercise 2: Impact of variable scale and distance measure {-}

Consider the 1-nearest neighbor algorithm to predict `Grad.Rate` on the basis of two predictors: `Apps` and `Private`. Let `Yes` for `Private` be represented with the value 1 and `No` with 0.

a. We have a test case whose number of applications is 13,530 and is a private school. Suppose that we have the tiny 2-case training set below. What would the 1-nearest neighbor prediction be using Euclidean distance?

```{r}
college_clean %>%
    filter(school %in% c("Princeton University", "SUNY at Albany")) %>%
    select(Apps, Private, Grad.Rate, school)

sqrt( (13530 - ?)^2 + (1 - ?)^2) # Euclidean distance
```

b. Do you have any concerns about the resulting prediction? Based on this, comment on the impact of variable scaling and the distance measure on KNN performance. How might you change the distance calculation (or correspondingly rescale the data) to generate a more sensible prediction in this situation?


### Exercise 3: Implementing KNN in `tidymodels` {-}

Adapt our general KNN code to "fit" a set of KNN models to predict `Grad.Rate` with the following specifications:

- Use the predictors `Private`, `Top10perc` (% of new students from top 10% of high school class), and `S.F.Ratio` (student/faculty ratio).
- Use 8-fold CV. (Why 8? Take a look at the sample size.)
- Use mean absolute error (MAE) to select a final model.
- Select the simplest model for which the metric is within one standard error of the best metric.
- Use a sequence of neighbor values from 1 to 100 in increments of 5 (20 values in total).


After adapting the code (but before inspecting any output), answer the following conceptual questions:

- Explain your choice for your recipe.
- Why is "fit" in quotes? Does KNN actually fit a model as part of training? (This feature of KNN is known as "lazy learning".)
- How is test MAE estimated? What are the steps of the KNN algorithm with cross-validation?
- Draw a picture of how you expect test MAE to vary with $K$, the number of neighbors. In terms of the bias-variance tradeoff, why do you expect the plot to look this way?

```{r}
set.seed(2021)





```

### Exercise 4: Inspecting the results {-}

- Use `autoplot()` to verify your expectations about the plot of test MAE vs. $K$, the number of neighbors.
- Contextually interpret the test MAE.
- How else could you evaluate the KNN model?
- Does your KNN model help you understand which predictors of graduation rate are most important? Why or why not?


### Exercise 5: Curse of dimensionality {-}

Just as with parametric models, we could keep going and add more and more predictors. However, the KNN algorithm is known to suffer from the "curse of dimensionality". Why? **Hint:** First do a quick Google search of this new idea.



