machine learning

what is a baseline model in machine learning 2024

Introduction

A baseline model is your first simple attempt at modeling, and it will provide you with a baseline metric that you will use as a reference point throughout development. This baseline model is often a heuristic (rule-based) model but could equally be a simple machine learning model.

What are baseline models and metrics in machine learning?

When creating a machine learning model, one needs to track error metrics to understand how accurate it is. However, one of data scientists’ biggest mistakes when building their models is throwing everything at the first attempt, making it difficult to know what made the model perform well or whether it is worth the time and effort invested.


So, what is a baseline model in machine learning?

A baseline model is your first simple attempt at modeling, and it will provide you with a baseline metric that you will use as a reference point throughout development. This baseline model is often a heuristic (rule-based) model but could equally be a simple machine learning model.


What are the benefits of creating a baseline model?

The benefits of creating a beginning model at the baseline of your work are two-fold 

  1. Understanding if the benefit is worth the cost
  2. Assigning performance improvements

Understanding the benefit vs. cost tradeoff is the main benefit of creating a baseline model at the beginning of your project. Machine learning models are expensive; this depends on the time it takes to develop and maintain them and the cost of tooling required to run them. So if the birth model is only, for illustration, five lower accurate than your completely fledged XGBoost model, is it worth the cost? Without the birth model for guidance, the XGBoost model would look emotional, but the birth model provides a precious environment. 

 The alternate crucial benefit, assigning performance advancements, is part of a wider trouble to reiterate over your model, where the birth is the starting point. 

 Knowing what feature engineering change or parameter tweak leads to which performance improvement is important for improving your understanding and where to focus your efforts.


What models work well as baseline models?

So, we’ve seen that baseline models are an important part of your workflow, but what is a baseline model? However, the most common approach is to try and find a rule-based approach, as this is often the most basic type of model that can be created.

How do I create a rule-based baseline model?

A rule-based approach will change depending on whether it is a classification or a regression problem, with regression problems often favoring statistics and classification problems favoring manually created decision trees.


Creating a rule-based baseline model for regression

Say we are trying to predict 12-month customer revenue on our eCommerce site. A baseline model could consist of calculating each customer’s income for the previous 12 months and using this as your prediction.

Creating a rule-based baseline model for classification

Imagine we are trying to predict whether a customer will churn from our subscription platform. A rule-based baseline model for this project: customers who have not been onto our platform for the past 30 days and have been customers for less than one year are predicted to churn, and those who don’t fulfill this criterion are not expected to churn.


How do I create a machine learning baseline model?

If the rule-based approach does not work for your project, then your next choice is to use a machine-learning model for your baseline instead. As we outlined earlier, this baseline model provides the simplest reference point to begin your development, so this model should be simple in terms of the features used and the model type.

If you’re solving a regression problem, I recommend using linear regression, and if you’re looking at classification, logistic regression is a good baseline model.


What should I do if my birth model is better than my final machine literacy model? 

It is extremely unlikely that a baseline model would outperform your final production-ready model; if this is the case, there is likely an error within the dataset you use for your machine-learning model. In this situation, I would look to see if you’re either:

  1. Leaking data into your baseline model predictions
  2. Preparing your features incorrectly

How much better does my machine education model need to be than my beginning model? 

Assuming that your machine learning model outperforms your birth model, this begs the question,” How much better should it be? 

“. As I mentioned earlier, the main reason for creating a baseline model is to understand if the machine learning model and all its costs are worthwhile. Of course, it completely depends on the context and the problem you are tackling, but I have a general rule of thumb that I use when looking at the final model performance metric improvement over the baseline:

  • Below 5% – Stick with the baseline model

  • 5 % to 10% – OK, but it depends on the use case

  • Over 10% – Good, stick with the machine learning model

06 Wait For Next Post

Leave a Comment

Your email address will not be published. Required fields are marked *