The challenges of developing Machine Learning and statistical models - Riskaware

The challenges of developing Machine Learning and statistical models

Granting us access to unparalleled insights, and informing critical decision-making, the capabilities of Machine Learning (ML) and statistical models enable a wealth of benefits.

However, when developing and producing these models, we must be aware of both the inherent challenges involved and risk of introducing unwanted biases through incorrect representation.

From navigating these complexities when training ML models, to ensuring that data collected is high-quality and secure, we encounter many of these challenges throughout incident modelling projects

Exploring these challenges, we hope to delve into more detail about how we continue to interact with, and enhance, our Machine Learning models – providing users with critical insights that facilitate true preparedness.

At a glance, these core challenges are:

  1. Lack of quality training data
  2. Incorrect training data
  3. Poor data quality
  4. Introduction of implicit biases

Read on to learn how Machine Learning, Artificial Intelligence (AI), and statistical models differ (and why we use a fusion of all three).

Challenge 1: Lack of quality training data

Decision trees, recommendation engines, natural language processing, and other ML functions requires training. This takes both data and time.

Accommodating these requirements might sound simple, but can be incredibly demanding – depending on the sophistication of the algorithm that you’re training.

An algorithm learns by being trained against a set of training data. For simple processes, this may require terabytes of data, while complex operations – such as the level of predictive analytics that our tools perform – requires even picobytes.

Data also needs to be of sufficient fidelity, quality, and breadth to accurately capture system behaviour. If these requirements aren’t met, models can’t effectively function – leading to more challenges and frustration.

As well as a lack of training data, users can also ‘overfit’ their training data. A very common challenge, overfitting training data means that ML algorithms are trained with ‘noisy’ datasets, which will introduce unwanted bias while affecting its overall performance.

Learn more: Why do we use AI, Machine Learning, and Statistical Models at Riskaware?

Challenge 2: Incorrect training methods

Machine Learning models can be trained using a wide variety of different methods – each complete with their own use cases, benefits, and disadvantages. If specialists choose to train their Machine Learning algorithms using the wrong method, tools may be inaccurate and feedback poor results. Becoming familiar with the different types of training models is therefore essential.

These learning methods include:

Supervised Machine Learning

Supervised Machine Learning requires, as expected, a ‘supervisor’ in the form of an ML specialist that collects and labels training data, before communicating the process to the algorithm. Once communicated and clarified, the supervisor must then repeat the test to ensure that the error has been successfully overcome, or decide if further iterations must be made.

In real-world use cases, Supervised Machine Learning tools are ideal for use in facial recognition software and stock price prediction tools.

Unsupervised Machine Learning

Unsurprisingly, Unsupervised Machine Learning algorithms don’t require a supervisor through the training process. These models are trained by being given a set of unlabelled raw data, and are instructed to find patterns and connections independently.

Common unsupervised ML use cases include fake or misleading news identification, or driving personalised recommendations – be it new entertainment, clothing, or food and drink venues.

Semi-supervised learning also exists as a hybrid between these two models, where algorithms are left unattended while supported with a small array of labelled training data. Use cases here include document classification and object localisation.

Reinforcement Machine Learning

Currently the most effective way to produce an estimation of a machine’s creativity, reinforcement learning involves the algorithm using ‘trial and error’ to reach a designated ‘reward’ – receiving positive or negative feedback that determines direction.

Self-driving cars and in-game AI are both examples of reinforcement learning-driven ML algorithms.

Without selecting the correct training model for ML algorithms, specialists will yield incorrect results or findings that do not suit the specific purpose.

Challenge #3: Poor data quality

Poor quality of data in leads to poor quality of data out.

If overall data quality is poor, users will experience poor results regardless of ML functionality – be it neural networks, broader deep learning methods, or other data science procedures.

Variation in results is commonly found at the top of any list of difficulties for data scientists in ML. This is why, along with ensuring that our training processes are refined, and our training dataset size is optimised, we make use of a rigid data governance strategy.

  • Who can access and modify your data?
  • How is it maintained?
  • How do you accommodate varying data types in a single location?
  • How often is data reviewed?

Answering all these questions and more can help inform a bespoke data governance strategy that ensures that any data used throughout ML and statistical modelling procedures are secure, reliable, and will provide unwavering results.

Challenge #4: Subjection to implicit biases

We already know that poor quality data can lead to poor results generated through Machine Learning, statistical modelling, and visualisation. But what about missing data?

With improper data governance or training processes, it can be easy to exclude vital data from algorithms – leading to an unintentional bias that can pose a serious threat to decision-making.

Bias continues to present a significant challenge throughout analytics, as ultimately it falls to the user to draw their own inferences, which is why we aim to make our reporting dashboards as intuitive and accessible as possible.

With clear, accurate results provided in a clear and distinguished format, bias can be successfully removed from interpretations to encourage a collaborative approach to incident modelling and response.

Read more: Why is collaboration key when responding to incidents?

Modelling advanced insights

Any Machine Learning or statistical modelling project comes with its unique challenges and critical considerations. Overcoming these is vital to eliminate unwanted bias – intentional or not – and gain access to actionable and innovative intelligence.

At Riskaware, our team of specialists deploy market-leading AI, ML, and statistical modelling processes to enable true preparedness – navigating advanced challenges to supply proactive and advanced insights.

To learn more, read our full range of insights here.

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