neuralGAM is a fully explainable Deep Learning framework based on Generalized Additive Models, which trains a different neural network to estimate the contribution of each feature to the response variable.

The networks are trained independently leveraging the local scoring and backfitting algorithms to ensure that the Generalized Additive Model converges and it is additive. The resultant Neural Network is a highly accurate and interpretable deep learning model, which can be used for high-risk AI practices where decision-making should be based on accountable and interpretable algorithms.

The full methodology of the method to train Generalized Additive Models using Deep Neural Networks is published in the following paper:

Ortega-Fernandez, I., Sestelo, M. & Villanueva, N.M.

Explainable generalized additive neural networks with independent neural network training. Statistics & Computing 34, 6 (2024). https://doi.org/10.1007/s11222-023-10320-5

and is also available in Python at the following Github repository.

neuralGAM is based on Deep Neural Networks, and depends on Tensorflow and Keras packages. Therefore, a working Python>3.9 installation with those packages installed is required.

We provide a helper function to get a working python installation from RStudio, which creates a miniconda environment with all the required packages.

```
library(neuralGAM)
install_neuralGAM()
```

In the following example, we use synthetic data to showcase the performance of neuralGAM by fitting a model with a single layer with 1024 units.

```
n <- 24500
seed <- 42
set.seed(seed)
x1 <- runif(n, -2.5, 2.5)
x2 <- runif(n, -2.5, 2.5)
x3 <- runif(n, -2.5, 2.5)
f1 <- x1 ** 2
f2 <- 2 * x2
f3 <- sin(x3)
f1 <- f1 - mean(f1)
f2 <- f2 - mean(f2)
f3 <- f3 - mean(f3)
eta0 <- 2 + f1 + f2 + f3
epsilon <- rnorm(n, 0.25)
y <- eta0 + epsilon
train <- data.frame(x1, x2, x3, y)
library(neuralGAM)
ngam <- neuralGAM(y ~ s(x1) + x2 + s(x3), data = train,
num_units = 1024, family = "gaussian",
activation = "relu",
learning_rate = 0.001, bf_threshold = 0.001,
max_iter_backfitting = 10, max_iter_ls = 10,
seed = seed
)
ngam
```

You can then use the `plot`

function to visualize the learnt partial effects:

`plot(ngam)`

Or the custom `autoplot`

function for more advanced graphics using the ggplot2 library:

`autoplot(ngam, select="x1")`

To obtain predictions from new data, use the `predict`

function:

```
n <- 5000
x1 <- runif(n, -2.5, 2.5)
x2 <- runif(n, -2.5, 2.5)
x3 <- runif(n, -2.5, 2.5)
test <- data.frame(x1, x2, x3)
# Obtain linear predictor
eta <- predict(ngam, test, type = "link")
# Obtain predicted response
yhat <- predict(ngam, test, type = "response")
# Obtain each component of the linear predictor
terms <- predict(ngam, test, type = "terms")
# Obtain only certain terms:
terms <- predict(ngam, test, type = "terms", terms = c("x1", "x2"))
```

If you use neuralGAM in your research, please cite the following paper:

Ortega-Fernandez, I., Sestelo, M. & Villanueva, N.M.

Explainable generalized additive neural networks with independent neural network training. Statistics & Computing 34, 6 (2024). https://doi.org/10.1007/s11222-023-10320-5

```
@article{ortega2024explainable,
author = {Ortega-Fernandez, Ines and Sestelo, Marta and Villanueva, Nora M},
doi = {10.1007/s11222-023-10320-5},
issn = {1573-1375},
journal = {Statistics and Computing},
number = {1},
pages = {6},
title = {{Explainable generalized additive neural networks with independent neural network training}},
url = {https://doi.org/10.1007/s11222-023-10320-5},
volume = {34},
year = {2023}
}
```