ML and Statistical Modelling – how it’s done - Riskaware

ML and Statistical Modelling – how it’s done

Previously, we’ve discussed the importance of deploying statistical modelling and Machine Learning (ML) tools to provide access to intuitive and proactive insights. Facilitating preparedness across a wide range of incident response use cases, modelling makes our work and suite of products possible. But there are many factors to consider when creating these models before they can be deployed.

Statistical modelling and Machine Learning, a quick definition

It can be easy to refer to Machine Learning models as statistical modelling, and vice-versa, but this can cause future misunderstandings.

Put simply, statistical modelling is the process of applying statistical analysis to a range of data. When done correctly, a model is a mathematic visualisation of given data. Machine Learning, on the other hand, involves training an algorithm alongside statistical approaches to make choices and decisions without human intervention. For example, when streaming services recommend songs or shows based on your previous interests, this is Machine Learning in action.

ML modelling takes this to the next level, enabling the ability to map and predict future outcomes based on historical and streaming data. These capabilities provide our users with the intelligence needed to respond to a vast array of incidents for timely and effective impact mitigation.

How are statistical models created?

Before any model is created, whether it’s Machine Learning or statistical analysis, two core factors must be reviewed:

  1. What is the end objective of the models?

By establishing a clear objective, users can ensure that models will continue to be useful and actionable long-term – making them a valuable asset that’s worth the investment.

2. What data is needed and how can we guarantee quality?

By considering the quality of data and what governance structures will be put in place throughout, users can guarantee reliability, and determine if any additional data is needed.

With these two questions answered, users can begin constructing models. But what model should be used?

The different models explained

Statistical modelling can take on many forms, each one suited for a specific objective. Knowing the end goals of your models can inform your selection, ensuring that they provide the insights needed to achieve actionable intelligence. Some of the most common models are regression and classification models.

Regression models

Regression models are used to examine the relationships between sets of variables, as well as to define which independent variables (the causes) have a knock-on effect on dependent variables (the affected subject).

When done correctly, these effects can be altered and observed. This produces information about trends that can be leveraged to make essential strategic decisions. There are many different forms of regression like linear regression, stepwise regression, ridge regression, lasso regression, and elastic net regression, and each form provides a unique insight from historical data.

Classification Models

As a form of Machine Learning, classification models are used to appropriately classify data in the hopes of making more accurate future predictions.

Using a pre-analysed historical dataset, classification models can employ understandings for future use. This is extremely important, as it enables users to move from historical data to streaming data for proactive capabilities like incident modelling.

Some forms of common classification models include:

  • Decision trees
  • Random forests
  • Nearest neighbour analysis

Complex and advanced types of classification models exist as well, such as neural networks.

Ensuring that models are value-aligned

Once clear objectives have informed the choice of model and type, users should make sure that all system points are aligned to bring as much value as possible. To do this, explore some or all of the following points:

  • Who will own this project?
  • What is project success defined as?
  • What type of problem will the model need to solve?
  • Is training data of an acceptable quality and quantity to fuel training?
  • How resilient are ML algorithms against cyber security threats?
  • Can this project use pre-trained models to save valuable time?

You may next be able to start construction and testing – provided your training data is of a high enough quality.

Models and data quality

When working with both statistical modelling and Machine Learning models, analysts need to ensure that data is consistently reliable and secure, so that all findings are accurate.

The quality of data has a significant effect on the overall effectiveness of models. If a model learns from poor data, it will be ineffective for real-world projects. It’s important to note that when we consider data quality, we don’t just mean value and structure – size has a distinct role.

Throughout construction and training, data should regularly be cleaned and checked for standardisation. In this process, any missing data will be identified to ensure unwanted bias is removed, and any outliers are detected.

Underfitting and overfitting data can have disastrous consequences on models. Learn more about these consequences here.

Consistent optimisation

Once your statistical models and Machine Learning tools have been deployed, it’s important to consistently optimise them based on findings to ensure that they remain high-quality, reliable, and relevant.

At Riskaware, we use statistical modelling and Machine Learning models to enable a wide range of use cases – from oil spill response to CBRNE mitigation.

Learn more about how we use Machine Learning models, statistical modelling, and Artificial Intelligence here.

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