Random Forests vs Neural Networks: Are you overcomplicating?
Random forests and neural networks are two popular machine learning algorithms used for classification and prediction tasks. While both algorithms have their strengths and weaknesses, they differ in many ways and are best suited to different types of problems. Let’s look at both of them and see why the more complex neural network isn’t always the best option.
RANDOM FORESTS
Random forests are an ensemble learning method that predicts by combining multiple decision trees. Each decision tree in the forest is built using a random sample of the training data, and the final prediction is made by taking the average of the predictions from each tree.
Random forests are well-known for their simplicity, versatility, and robustness and are widely used for both regression and classification tasks.
Why to choose Random Forests?
- Easy to interpret: Random forests are easy to interpret, as the individual decision trees are simple and straightforward.
- Handle missing data well: Random forests can handle missing data in the training data, as each decision tree is built on a random subset of the data.
- Robust to outliers: Random forests are robust to outliers, as the final prediction is made by averaging the predictions from multiple trees.
NEURAL NETWORKS
Neural networks are machine learning algorithms based on the structure of the human brain. They are made up of several layers of artificial neurons, and the connections between them are used to learn patterns in data. For complex tasks such as image recognition, speech recognition, and natural language processing, neural networks are widely used.
Why to choose Neural Networks ?
- Handle complex relationships: Neural networks can handle complex relationships between input and output variables, as they have the ability to learn and model these relationships.
- Scale well: Neural networks can scale well, as they can be easily extended to include more layers and neurons to handle more complex tasks.
- Good at dealing with large amounts of data: Neural networks are good at dealing with large amounts of data, as they can learn patterns and relationships in the data through backpropagation.
ARE NEURAL NETWORKS ALWAYS BETTER?
Although neural networks might get your job done they are better suited for complex problems with complex relationships between input and output variables. While random forests are best suited to simple problems with clear relationships between input and output variables
Random forests are also easy to interpret, as each decision tree is simple and straightforward. This makes it possible to comprehend how each input variable contributes to the generation of a specific output.
On the other hand in neural networks, because the relationships between the input and output variables are learned through a series of transformations, it becomes hard to keep track of them, making neural networks difficult to interpret.
In terms of performance, neural networks are usually more accurate, but random forests are faster because they are based on simple decision trees. Random forests are also less computationally expensive than neural networks.
CONCLUSION
So, the next time you need to perform a machine learning task on simple tabular data with clear relationships, it could be worth giving random forests a shot before turning to neural networks.