Neural networks remain one of the most powerful tools in the data scientist’s arsenal. They capture non-linear relationships, adapt as data changes, and can power everything from recommendation engines to risk models. But to really use them well, you need more than just raw accuracy—you need interpretability, robustness, scale, and clarity. This article walks you through how to build neural networks in R in 2025, how to visualize and understand them, and how to evaluate their performance reliably.
What’s Changed Since the Earlier Days
Before digging in, here are some shifts in how neural networks are being used and built today:
- Easier access to scalable backends: With packages that interface with TensorFlow or Keras, or via endpoints built for R, heavy models can be trained off-device or off-machine while R is used for prototyping, visualization, and downstream work.
- Better interpretability tools: Techniques like SHAP values, partial dependence plots, and layer-wise relevance propagation are more accessible. Understanding why a model makes predictions is increasingly necessary.
- More attention to normalization, regularization, and overfitting: Dropout layers, batch normalization, careful tuning of architecture size—all standard practice now.
- Cross-validation and resampling is non-negotiable: Multiple train/test splits, fold validation, or even nested cross-validation are expected, especially in business or research settings.
Step-by-Step: Building & Visualizing a Neural Network in R
Here’s a modern workflow from data preparation through visualization and evaluation.
1. Data Preparation
- Select relevant features and remove or encode features that aren’t numeric.
- Normalize or scale features: Use techniques like min-max scaling, z-scoring, or robust methods (median and MAD) to reduce the influence of features with large numeric ranges.
- Train/Test/Validation split: Instead of only one train/test split, keep aside a validation set or use k-fold cross-validation to avoid overfitting.
library(dplyr)
data <- read.csv("cereal_data.csv")
Handle missing values
data_clean <- data %>%
filter(!is.na(rating)) %>%
mutate_at(vars(calories: fiber), ~ if_else(is.na(.), median(., na.rm = TRUE), .))
Split
set.seed(123)
n <- nrow(data_clean)
train_index <- sample(seq_len(n), size = 0.60 * n)
train_data <- data_clean[train_index, ]
test_data <- data_clean[-train_index, ]
2. Scaling
Example of min-max scaling
scale_minmax <- function(x) {
(x - min(x, na.rm = TRUE)) / (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
}
train_scaled <- train_data %>% mutate_at(vars(calories: fiber), scale_minmax)
test_scaled <- test_data %>% mutate_at(vars(calories: fiber), scale_minmax)
3. Define the Neural Network Architecture
- Choose number of hidden layers (1 or more), number of neurons, activation functions (ReLU, tanh).
- Incorporate regularization such as dropout or weight decay if using deeper networks.
In R, you might use packages like neuralnet, or via wrappers to Keras/TensorFlow for more complex architectures.
library(neuralnet)
Simple network with one hidden layer of, say, 3 neurons
set.seed(456)
nn_model <- neuralnet(rating ~ calories + protein + fat + sodium + fiber,
data = train_scaled,
hidden = 3,
linear.output = TRUE)
4. Visualizing the Network
- Plot the network structure: show input → hidden → output nodes, with connection weights.
- Use color or thickness of edges to represent magnitude of weights.
- Use diagrams to show bias terms, activation, etc.
plot(nn_model)
This gives a simple visualization. For deeper insights, use tools that can extract and plot weight magnitudes or feature importance.
5. Prediction & Rescaling
Since we scaled features, predictions will also be scaled. To interpret the results, retransform predicted values back to the original scale:
Prediction
pred_scaled <- compute(nn_model, test_scaled[, c("calories", "protein",
"fat", "sodium", "fiber")])$net.result
Reverse scaling of rating (assuming you know its min and max in original data)
orig_min <- min(data$rating, na.rm = TRUE)
orig_max <- max(data$rating, na.rm = TRUE)
pred <- pred_scaled * (orig_max - orig_min) + orig_min
6. Evaluation
- Compute metrics like Root Mean Square Error (RMSE), Mean Absolute Error (MAE).
- Use visual diagnostics: scatter plot of predicted vs actual. Add a 45-degree line.
- Evaluate on validation set or via cross-validation: improve robustness.
rmse <- sqrt(mean((test_data$rating - pred)^2))
7. Cross-Validation & Robustness Testing
- Use k-fold CV, repeated splits, or bootstrapping.
- Evaluate how RMSE or error metrics change with different training sample sizes.
- Monitor overfitting: check if training error drops but validation error rises.
8. Interpretation & Explainability
- Analyze feature importance by looking at weights or by perturbation: how does changing a feature slightly affect the output?
- Use partial dependence plots to see how each feature influences the prediction when others are held fixed.
- Consider SHAP or LIME-style techniques (if integrating with packages or via R wrappers) for local interpretability.
Best Practices & Modern Improvements
- Regularization: Techniques like L1 or L2 penalty, dropout, or early stopping during training are standard.
- Hyperparameter tuning: Number of hidden units, learning rate, number of layers—all should be tuned via grid search or Bayesian optimization.
- Model stacking / ensembles: For many real-world problems, combining neural networks with more explainable methods (like tree-based models) improves both accuracy and interpretability.
- Monitor bias & fairness: Ensure that the network doesn’t unfairly under-predict or over-predict for certain subgroups (age, demographic). Check predictions vs actuals across these segments.
Practical Limitations & Considerations
While neural networks offer flexibility and power, they come with trade-offs. They tend to require more data to avoid overfitting, especially with more layers or neurons. Training can be more computationally intensive, especially for deeper architectures. Interpretability is trickier: weight magnitudes only tell part of the story; often additional tools are needed to understand feature influence. Hyperparameter tuning can be time-consuming. Results are sensitive to scaling, initialization, and the specifics of architecture choice. For many business problems, simpler models can sometimes perform comparably well and be far easier to explain and deploy.
Conclusion
Neural networks in R remain a powerful tool for modeling non-linear relationships. In 2025, the expectations go beyond just fitting them—you need to make them interpretable, robust, and reliable. By carefully preparing data, choosing architecture thoughtfully, visualizing what’s happening under the hood, evaluating thoroughly, and guarding against overfitting, you can build models that are not just accurate—but trustworthy.
This article was originally published on Perceptive Analytics.
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