How To Implement Baselines In ML Modeling And Why We Need Them?

Have you ever wondered why admission tests have cut-offs? Why are they fixed at a specific number? We establish a similar cut-off for our models in machine learning, and if the model falls below that number, we know the model is in bad shape.

It's known as a baseline model, and in this article, we'll discover what it is, why it's important, and how to use it.

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What is a Baseline Model?

Baseline models serve as a benchmark in an ML application. Their main goal is to put the results of trained models into context.

Assume you begin working on a problem statement and complete all of the steps, including EDA, data cleansing, and feature engineering. You now begin working on your model. During model training, you discover that your model's accuracy is 54%. So, without making much effort, you now have a 54% accuracy level, which is now your base value.

You can now tag this as a baseline model, indicating that you will enhance this number after this. If your model's accuracy level goes below 54% in the future, it means the model requires improvements.

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Types of baseline models

Baseline models are divided into three main categories:

• Random Baseline Models: Data in the actual world isn't always reliable. A dummy classifier or regressor is the optimal baseline model for these issues. This baseline model will inform you if your machine learning model is learning or not.
• ML Baseline Modes: Now, if the data is predictable, you can create a baseline model which helps us analyze which features are critical for prediction and which are not. The baseline models are commonly used with feature engineering.
• Automated ML Baseline Models: It is the ultimate baseline model. It's an excellent model for comparing your ML model. If your ML model outperforms the automated baseline model, it's a strong indication that the model has the potential to become a product.

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Why do We Need a Baseline Model?

Before we get into the benefits of baselines, let's go over a few key points for choosing baselines:

• Baselines provide you a lower limit on what you may expect from your model.
• The more valuable the baseline is, the closer the lower bound is. Carefully tuned pipelines, published outcomes, and human baselines, for example, are all preferable.

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Benefits of baseline models

Understand your data

The key advantage of employing the baseline model is that it aids in data comprehension:

• Analyze observations that are challenging to categorize: With the help of a baseline model, you'll be able to figure out which observations are difficult to categorize.
• Analyze the different classes: Likewise, if you're focusing on a multi-class regression issue, a baseline model might show you which classes are simple to classify and which are tough to classify.
• Detect data with low signal strength: A weak signal or low fitting might be indicated by a baseline model with no or little prediction.

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Faster iteration

Baseline models also help improve the efficiency with which you can build the models.

• Increase speed and performance: With a baseline model in place, you will have detailed information on what to improve and develop. This makes it easy to see if the changes you're making to your model are improving metrics or not. This enables you to quickly discover initiatives that can enhance your KPIs.
• Efficiency:  If you build a baseline model, the amount of work you have to do on current projects may reduce, allowing you to focus on other projects. The baseline model facilitates efficiency and productivity.

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Performance benchmark

Baseline models provide a suitable standard against which you can evaluate your real models.

• Some performance measures, such as logarithmic loss, are helpful to evaluate amongst models than to assess individually. This is due to the fact that many performance measurements lack a specified scale and instead take on varying values based on the result variable's range. This can assist you in determining when a sophisticated model is required vs when simple business logic is adequate.
• Calculate the impact on key business parameters. Creating a simple baseline model can also help you see what type of influence you might have on company indicators. This is particularly true if your baseline model is stochastic as well.

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Building a baseline model

There are a few different ways to create a baseline for your models:

Rule-based models

As the name implies, rule-based models produce predictions based on basic rules. The sort of rule that a model can use is determined by its purpose. You can also build a model that makes random or constant predictions of your choice, although this method is less common because it does not take advantage of domain expertise.

This strategy is popular due to its prediction delivery, but it has a few drawbacks, such as the fact that it ignores input data, which might have an influence on your problem statement.

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Baseline regression models

Let's look at a few baselines that may be utilized to solve regression difficulties.

• Mean or median: You can use mean or median as baseline for your outcome. 
• Business or Conditional Logic: In this you consider 1-2 factors for your problem. For example, if you are working on a model that analyzes the height and weight of a child. In this you can take a base value like an average 5-7 year old child's height is around 39 to 48 inches. This can help you achieve your problem statement or business goals.
• Linear regression: If you're using a sophisticated model with a large dataset as your primary model, a simple linear regression model with a few parameters might be a good baseline model.

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Baseline classification models

Let's look at a few baselines that may be utilized to solve classification difficulties.

• Mode: The simplest baseline model for binary classification problems is just predicting the mode of the outcome variable for all data. 
• Business or Conditional Logic: You take into account a few elements in order to solve your problem. For example, if you want to know how much milk a cat drinks in a day. So, you can classify into two groups with more than two liters but fewer than two liters . So, to work on this model, you may divide them by cat size. Large cats, for example, have been known to consume more than two liters.
• Logistic regression: If your classification model contains a lot of features, a basic model like a logistic regression model might be used as a good starting point.

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Human as baseline works perfect, so how to create a good human baseline? Let's go through a few crucial factors to help you establish a decent human baseline:

• Random People from different online sites/tools.
• Domain Experts (Doctors, Agents etc).
• Deep Domain experts (Specialists, Researchers) 

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Use of Monitoring

AI usage is accelerating in a variety of areas. However, the difficulty of applying machine learning has hampered AI systems' performance. MLOps, particularly the productionization of machine learning models, face issues comparable to those that plagued software before DevOps Monitoring.

ML monitoring of models helps you to quickly identify outliers and determine which ones are essential, whether they pose a threat or not. Censius helps your models to continuously monitor for drifts, data changes, and performance measures, and the model owner is notified.

• Keep an eye on the model's inputs and outputs at all times.
• Check for data integrity across the pipeline and keep track of predictions, data, and concepts.
• Monitor transgressions and receive real-time notifications.

Get access to Censius today. 

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Conclusion

In general, a baseline model is useful for determining the consistency of any trained model. It also aids in the evaluation of the data set in question's usefulness. As a result, the baseline model should always be the first model you develop in a machine learning project. In this article, we saw what the baseline model is, the benefits of it, and how to build it. Hope you liked the article.