There are various algorithms when solving any Machine Learning (ML) problem. Do you remember the “no free lunch” theorem? In this context, no single algorithm can solve all problems.

The performance of ML algorithms depends on your data size and structure. Therefore, the correct algorithm is unclear until you test your algorithm through trial and error. Each algorithm has advantages and disadvantages, guiding you to find the most suitable one.

No algorithm is better or worse than the other. However, each algorithm has some properties that can help you pick the right one quickly and tune hyperparameters.  In this blog, we will discuss how a few prominent ML algorithms can help with regression issues and how to leverage them based on their strengths and weaknesses.

Hopefully, this article helps you find the best ML algorithm for your regression problem!

Linear and Polynomial Regression

In simple cases, Single Variable Linear Regression stimulates the relationship between a single input independent variable and an output dependent variable using a linear model, i.e., a line. However, you can use a Multi-Variable Linear Regression model in more general cases.

In this, a model shows the relationship between multiple independent input variables and an output variable. It is a linear model, and the output is a linear combination of input variables. A third general case is called the Polynomial Regression – this model is a non-linear combination of the variables. It can have exponential variables, sine, and cosine.

Creating a Polynomial Regression model requires knowledge of data relativity and output. You can train these models using the Stochastic Gradient Descent (SGD).

Pros:

-Can model quickly and is beneficial when the relationship is not very complex and/or if you don’t have much data.

-Linear progressions are comprehensible and straightforward data projections, which are valuable when making business decisions.

Cons:

-It can be quite complex to design a polynomial regression, when dealing with non-linear data. You need knowledge about data structures and the relationship between the variables.

-Therefore, these models are not good for highly complex data.

Neural Networks

Neural networks have neurons – an interconnected group of nodes. These neurons receive input feature variables from the data as a multi-variable linear combination. In this process, the values multiplied by each feature variable are called ‘weights’.

The neurons then apply a non-linearity which enables the network to model complex non-linear relationships. Neural networks may have multiple layers where each layer passes the output to the other. The output does not have any non-linearity. You can train Neural networks using the Stochastic Gradient Descent (SGD) and the backpropagation algorithm.

Pros:

-Since neural networks are layered with non-linearities, they are suitable to model complex non-linear relationships.

-There is no need to worry about data structures as neural networks are flexible. They can learn almost any kind of feature variable relationships.

-Research consistently shows that giving the network more training data, whether new or augmented, enhances the network performance.

Cons:

-The models can be challenging to interpret and comprehend as the models are very complex.

-They are very computationally intensive to train and need careful hyper-parameter tuning and learning rate schedule setting.

-This model requires high amounts of data to achieve superior productivity and outperform other ML algorithms in “small data” cases.

The Bottom Line

Here are the various pros and cons of different ML algorithms. Know that these algorithms are not all there is. There are many more for you to try and see if it suits your needs. We hope you found it helpful. Let us know in the comments if we left something out.