Adopting machine learning offers little benefit if their predictions provide few actionable insights. Many companies simply rush through the model development process as quickly as possible with little time spent on training and testing them before deployment. The fact so many organizations continue to derive significant benefits from AI is likely contributing to this hurried work from these late adopters.
Of course, before improving machine learning predictions you first need the ability to track the efficacy of your ML models after deploying them into production. This approach requires determining the right metrics as well as a system for reporting them. Leveraging a concept called Explainable AI (XAI) also helps model developers and users understand why certain predictions get made; ultimately improving their predictive quality.
So let’s analyze how to develop machine learning models that are explainable, easily trained, and ultimately provide accurate predictions. Those models are then deployed into production with confidence. In the end, this approach helps companies improve their decision-making acumen, leading to a more productive and profitable business.
Before starting work on building a machine learning model, performing an initial analysis of the underlying data is essential. After all, developers are simply flying blind without a clear understanding of the data, its strengths and weaknesses, outliers, correlation, as well as any missing elements. This process ensures the overall data quality before feeding it into the model.
Leveraging explainable ML algorithms also facilitates the model training process, as noted earlier. Without it, the model generation only serves as a black box, leaving developers and stakeholders wondering why a model generates poor predictions when in production. Ultimately, XAI provides the necessary understanding for building effective and actionable machine learning models.
Once a newly created model is ready to be trained, the user creates a predictor using the MindsDB Studio tool. This effort simply involves selecting a dataset, one or more data columns, and entering a name for a predictor. The tool then starts a process that extracts the data; followed by cleaning and analyzing it.
This processed data then gets used to train the model, using the tool’s embedded XAI algorithms. Our tool displays the final output in an easy-to-understand report, illustrating the overall efficacy of the model. The user sees at a glance a General Accuracy Score as well as other useful information, including column importance, column irrelevance, and any relationships or correlations between different columns.
Building the Users’ Confidence in a Machine Learning Model
It’s not surprising that machine learning tools need to engender trust in those using them. After all, any new technology, especially something related to artificial intelligence, sometimes creates a measure of distrust. When it comes to ML models, building confidence through runtime monitoring and XAI helps business stakeholders use a model’s prediction to inform their decision-making process without worry.
Older machine learning tools in place when the technology first emerged didn’t use XAI. Instead, they typically just generated predictions in a black box without any additional information to support their conclusion. This lack of explainability hampered a user’s ability to trust the efficacy of a model’s prediction.
In fact, a recent study from IBM Research AI revealed that a machine learning tool simply providing a confidence score for the model increased the trust level of humans using the tool for decision making.
The study also noted that a confidence score itself didn’t improve the quality of decision-making. However, this confidence score can be improved through runtime monitoring to highlight where and why the model made errors.
Ultimately, a machine learning tool leveraging XAI and an informative user interface play a key role in boosting the confidence of stakeholders using ML models for business decision-making. Enhancing this information with visualizations also aids in generating trust with users. This provides an extra measure of transparency; helping the overall understanding on why the model made certain predictions and specific conclusions.
This extra level of usability helps the user quickly identify any biases in the data leading to the model making incorrect predictions. They are then able to quickly add modifications to make the ML model more reliable, leading to improved predictions and a more informed decision-making process.
The GUI used in our MindsDB Studio tool also provides helpful visualizations of the model’s output. This enables the user to share this information with other business stakeholders, providing a helpful confidence boost regarding the overall efficacy of the model’s predictions. As a result, any organization is able to trust the accuracy of their AI-assisted business decisions.
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