How can you incorporate human expertise into predictive analytics models?

If you do not provide human expertise in your model, it will start learning from scratch, which results in wasting time and effort. But if you provide human expertise, the model has the potential to achieve more high-level findings starting from your points.

Human expertise is indispensable in predictive analytics.It offers contextual intelligence that algorithms can't glean from data alone.Experts inject nuanced judgment, ensuring models align with domain realities.For instance,in healthcare, seasoned clinicians can flag variables that,while statistically significant,may be clinically irrelevant. LinkedIn articles emphasize that incorporating human intuition doesn’t diminish data-driven insights but complements them,leading to robust, interpretable models.There’s a symbiosis here algorithms learn patterns, humans provide wisdom a blend that sharpens the precision of predictive analytics. This human-in-the-loop approach is critical for models that withstand real-world scrutiny and complexities.

Whether or not human expertise matters and to what extent it does has been an increasingly hot topic recently. Time and time again novel algorithms solving problems in a simpler, end-to-end manner have proven to be more effective than their predecessors which largely relied on human expertise. Rich Sutton, in his article "The Bitter Lesson", gives notable examples of this happening in Chess, Go, and speech recognition. Models that did away with human expertise won out in a big way in those and many more cases. That said, not all problems are necessarily well-suited for simple, end-to-end solutions. The most common type of data we work with - small/medium tabular datasets - human expertise is invaluable!

Without the human factor, predictive models are like automobiles without engines. Humans provide the essence of predictive models by adjusting them in accordance with KPIs and business goals. Raw calculations and algorithms do not mean much without constant human interaction.

Human intervention is vital in enhancing these models. Experts can inject domain-specific insights and critical thinking to address and refine the limitations of these algorithms, much like human contributions to this AI-powered article. This collaborative effort between human judgment and machine precision not only fortifies the model's accuracy but also ensures adherence to ethical norms and legal standards, especially now in the wake of EU AI act.

Ground truth and data validation are invaluable means of incorporating human expertise into predictive analytics. If the fuel (data) is not pure the machine (model) will not operate correctly and efficiently.

Like many other data workflows, establishing a successful predictive model depends heavily on clear explanation. There should be a steady coordination between the business and technical teams. The data-tech team which is building the model should have a clear goal that meets the requirements of the business.

In addition to the point mentioned by other contributors, I’d like to add two things: ask questions, listen to the answers and have conversations with both experts and other stakeholders. Continuing with the health care example: We need to actively engage with doctors and health care professionals throughout the process. They are not only the domain experts, they are also likely to be representative of the uses of our predictive analytics. Their input and engagement is mission critical. We also need to engage with health care patients. They have a different perspective to the professional and ultimately it’s the patient who needs to derive quantifiable benefits for our predictive analytics to be successful.

Forming the bulk of a predictive models is usually easy. It is the fine-tuning that counts. Human expertise allows data teams to build predictive models that go beyond an average quality. Through our extensive understanding, we are able to optimize and enhance models to achieve outstanding results.

Incorporating human expertise into data can be effectively done through feature engineering. Take a dataset with age, height, weight, and a heart disease flag. These fields alone aren't too informative. However, after consulting a domain expert, you might learn about BMI, which can be calculated from height and weight. Their insight enables new ways to combine features, like the BMI in this case, improving your model's performance significantly. This is a basic example. In practice, experts can devise complex methods to transform existing data into something far more useful.

The question and responses from others are absolutely correct, but I’d like to add a “soft” or psychological answer. People are unlikely to use or trust a tool they don’t understand. People are inherently suspicious of “black boxes”. This is a challenge for data scientists as in many instances we almost speak a different language. Engagement helps stakeholder buy-in. There is always a group of stakeholders who are opinion leaders. By involving that group in the predictive analytics development and deployment process from inception, this group becomes “invested” in the predictive analytics too and the probability of successful adoption is increased.

- Mix expert advice into data predictions, we can use surveys or talk to experts to get their take, hold group discussions for deeper dives, or ask a bunch of people online for a wider range of ideas. Kind of what LinkedIn is doing with these collaborative AI powered articles. - Make sense of what experts say by asking clear questions, using rating systems, or even brainstorming with teams - Decide what recommendation is best, by using group votes, scoring systems.

Incorporating human expertise into algorithms involves marrying domain knowledge with data science. Feature engineering is pivotal here, where expertise informs data attribute selection and transformation. During model training, experts refine algorithms through feedback loops. Techniques like rule-based logic and interactive platforms allow direct embedding of insights, bolstering model accuracy and fostering transparency—essential tenets in LinkedIn's data science discourse. This collaboration ensures models truly reflect domain-specific nuances.

- Rule-based systems use rules set by experts to guide the prediction algorithms. This is like having a manual of "do's and don'ts" that the computer follows. - Bayesian networks rely on experts to set starting points for what the algorithms think is likely to happen, almost like setting the initial odds for a bet before seeing how the race plays out. - Ensemble methods use experts to pick and mix different algorithms or results to get the best prediction, kind of like a coach picking the best players for a team to win the game.

will also need to be aware of baking human bias into algorithms. Cross checking and diverse heuristics collection is needed to minimize bias.

To incorporate human expertise into algorithms, the Bayesian networks and ensemble methods are more efficient than Rule-Based Systems. Though rule-based systems are easy to understand and interpret due to their explicit rules, they can become complex and unwieldy for large or intricate decision-making processes. They may not generalize well to unseen data or handle uncertain information. Bayesian networks can handle uncertainty and probabilistic reasoning effectively. Ensemble methods that combine multiple models, each trained on different subsets of data or with different parameters, can improve accuracy and reduce overfitting. They can handle complex and heterogeneous data to capture diverse perspectives and expertise.

Human experts can tell you the relationships expected or perceived in the data. For example, when a polynomial relationship between an attribute and prediction is expected (i.e. most relationships where "an optimal attribute value" exists are like this) than you cannot expect algorithms based on linear functions (e.g. linear regression) or exponential functions (e.g. sigmoids-based neural networks) to be able to come up with good predictions. Hence, different polynoms-based or radial-functions-based algorithms or core nodes should be incormporated into the algorithm.

To weave human expertise into predictive analytics outcomes, initiate with context-enriched data interpretation. Experts' insights lead to nuanced metrics, refining algorithms beyond raw data analysis. They provide qualitative overlays to quantitative findings, guiding outcome applicability to real-world scenarios. Iteratively validate these expert-influenced outcomes against business objectives, ensuring actionable intelligence. This symbiotic interplay elevates the model's strategic value—a practice highly regarded in LinkedIn's analytical discourse. Such expert-augmented analytics become indispensable for precision-driven decision-making in complex environments.

One good point to add human expertise into the outcomes of data analytics is enabling an easy channel to suggest different aggregations or visualizations to existing dashboards or reports. Sometimes a good idea is discarded because the consumer just did not want to jump through all the loops to be heard. Adding a simple Google Doc Form link or embedding a text box to send suggestions is a game changer.