```
library(tidyverse) # Data wrangling
library(tidyfit) # Auto-ML modeling
```

The fitted model object is contained in `tidyfit.models`

frame in the `model_object`

column as an `R6`

class. The `tidyFit`

`R6`

class contains both the
underlying model (`...$object`

) as well as additional
information generated during fitting and needed to obtain predictions or
coefficients.

Suppose, for instance, we want to visualize the regression tree of
the hierarchical features regression for different degrees of shrinkage
(see `?hfr::plot.hfr`

). We begin by loading Boston house
price data and fitting a regression for 4 different shrinkage
parameters. Note that we do not need to specify a `.cv`

argument, since we are not looking to select the optimal degree of
shrinkage:

```
<- MASS::Boston
data <- data %>%
mod_frame regress(medv ~ ., m("hfr", kappa = c(0.25, 0.5, 0.75, 1))) %>%
unnest(settings)
mod_frame#> # A tibble: 4 × 6
#> model estimator_fct `size (MB)` grid_id model_object kappa
#> <chr> <chr> <dbl> <chr> <list> <dbl>
#> 1 hfr hfr::cv.hfr 1.71 #001|001 <tidyFit> 0.25
#> 2 hfr hfr::cv.hfr 1.71 #001|002 <tidyFit> 0.5
#> 3 hfr hfr::cv.hfr 1.71 #001|003 <tidyFit> 0.75
#> 4 hfr hfr::cv.hfr 1.71 #001|004 <tidyFit> 1
```

`kappa`

defines the extent of shrinkage, with
`kappa = 1`

equal to an unregularized least squares (OLS)
regression, and `kappa = 0.25`

representing a regression
graph that is shrunken to 25% of its original size, with 25% of the
effective degrees of freedom. The regression graph is visualized using
the `plot`

function.

Let’s examine the first model in the `tidyfit.models`

frame:

```
$model_object[[1]]
mod_frame#> <tidyFit> object
#> method: hfr | mode: regression | fitted: yes
#> no errors ✔ | no warnings ✔
```

We have two options to plot the regression trees. Many generics
function directly on the `tidyFit`

class. Therefore, we could
simply plot (in this case the unregularized regression graph):

```
%>%
mod_frame filter(kappa == 1) %>%
pull(model_object) %>%
1]] %>%
.[[plot(kappa = 1)
```

The regression graph shows which variables have a similar explanatory effect on the target (variables that are adjacent have a similar effect). The sizes of the leaf-nodes represent the absolute size of the coefficients.

Alternatively, we could access the underlying `cv.hfr`

object using `...$object`

:

```
<- mod_frame %>%
mod_frame mutate(mod = map(model_object, ~.$object))
mod_frame#> # A tibble: 4 × 7
#> model estimator_fct `size (MB)` grid_id model_object kappa mod
#> <chr> <chr> <dbl> <chr> <list> <dbl> <list>
#> 1 hfr hfr::cv.hfr 1.71 #001|001 <tidyFit> 0.25 <cv.hfr>
#> 2 hfr hfr::cv.hfr 1.71 #001|002 <tidyFit> 0.5 <cv.hfr>
#> 3 hfr hfr::cv.hfr 1.71 #001|003 <tidyFit> 0.75 <cv.hfr>
#> 4 hfr hfr::cv.hfr 1.71 #001|004 <tidyFit> 1 <cv.hfr>
```

Now there is a column with `cv.hfr`

objects. This is
useful, when we want to perform any analysis not directly implemented in
the `tidyFit`

generics.

Finally, we can use `pwalk`

to compare the different
settings in a plot:

```
# Store current par before editing
<- par()
old_par
par(mfrow = c(2, 2))
par(family = "sans", cex = 0.7)
%>%
mod_frame arrange(desc(kappa)) %>%
select(model_object, kappa) %>%
pwalk(~plot(.x, kappa = .y,
max_leaf_size = 2,
show_details = FALSE))
```

```
# Restore old par
par(old_par)
```

Notice how with each smaller value of `kappa`

the height
of the tree shrinks and the model parameters become more similar in
size. This is precisely how HFR regularization works: it shrinks the
parameters towards group means over groups of similar features as
determined by the regression graph.