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The Biggest Problem with Machine Learning Models that No One is Talking About and How to Fix It…
- Machine Learning Models (MLMs) have limitations such as hallucinations, fake alignment, and open-ended outputs, making them unsuitable for many real-world scenarios.
- Classical ML models also face challenges not widely addressed by ML practitioners, impacting their applicability in diverse scenarios.
- The problem of making predictions in real estate markets like Pima County, Arizona, highlights issues in model outputs based on the neighborhood's characteristics and sales history.
- Interpolation and extrapolation play vital roles in determining the reliability of ML model predictions based on the similarity to training data observations.
- Lack of attention to interpolative versus extrapolative predictions is a prevalent issue in AI projects, posing risks in modeling outcomes and decision-making.
- Zillow's venture into iBuying with Zestimate-led house valuations faced challenges due to inaccuracies in predicting property values, leading to substantial financial losses.
- Bayesian Machine Learning introduces a probabilistic approach that considers uncertainties in model predictions by incorporating variance to indicate confidence levels.
- Bayesian methodologies offer a solution to identify high-variance predictions and filter out unreliable estimations, enhancing decision-making processes in automated evaluations.
- Incorporating Bayesian techniques, like Zillow updating Zestimate to provide a range of possible sale prices, showcases the benefits of leveraging probabilistic models in real-world applications.
- Recognizing the importance of distinguishing between interpolative and extrapolative predictions is crucial for improving the reliability and accuracy of ML models in various domains.
- Further discussions on Bayesian versus frequentist ML models will be explored in Part 2 of the article series, emphasizing the advantages and drawbacks of each approach.
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