By Hari Haran
I’m gonna make it more theoretically ,As data science and machine learning become more widespread, there are now many different techniques available for modeling and prediction. Among these are linear regression models and neural networks, two popular methods for predicting values given a set of input features.
While neural networks have gained a lot of attention in recent years due to their impressive performance on many tasks, there are still several good reasons to consider using a linear regression model instead. In this blog post, we will explore 3 advantages of choosing linear regression models over neural networks.
Simplicity and Interpretability
Linear regression models are simple, easy to understand, and interpret. They have been around for a long time and are well understood, which means that they have a lot of documentation and are easy to troubleshoot if something goes wrong.
This simplicity also extends to the model itself: the equation for a linear regression model is straightforward and can be easily explained to non-technical stakeholders. In contrast, neural networks can be very complex, with many layers and thousands of parameters, making them difficult to interpret and explain to non-technical stakeholders.
Training Speed Linear regression models are much faster to train than neural networks, particularly for datasets with many features.
This is because the computation required for training a linear regression model is linear with respect to the number of features, whereas the computation required for training a neural network is exponential with respect to the number of layers and nodes.
This means that for large datasets, a linear regression model can be trained much more quickly than a neural network, making it a practical choice for time-sensitive applications.
Avoiding Overfitting Neural networks are powerful models that can learn complex relationships between input features and output values, but this power comes at a cost. Because they have so many parameters, neural networks are very prone to overfitting, which occurs when the model learns the noise in the training data rather than the underlying patterns.
Overfitting can lead to poor generalization performance, where the model performs well on the training data but poorly on new, unseen data. In contrast, linear regression models have fewer parameters and are less prone to overfitting, making them a more reliable choice for many applications.
Verdict
While neural networks are a powerful tool in machine learning, there are still many good reasons to consider using a linear regression model instead.
Linear regression models are simple, easy to interpret, and faster to train, and they are less prone to overfitting, making them a reliable choice for many applications. Of course, there may be cases where neural networks are the better choice, but it’s always a good idea to consider all available options and choose the one that is best suited for the task at hand.