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An error occurred while saving the comment Overfitting in machine learning occurs when a model learns the training data too well, capturing noise and outliers instead of the underlying pattern. This results in high accuracy on the training set but poor performance on unseen test data because the model fails to generalize. Overfitting happens when the model is too complex relative to the amount of data, such as having too many parameters or layers. Techniques to combat overfitting include simplifying the model, using regularization methods, employing cross-validation, and augmenting the training data.
In IoT analytics, data science is crucial for processing and interpreting vast amounts of data from connected devices. It involves collecting data from sensors and IoT devices, cleaning and transforming this data, and applying statistical and machine learning techniques to extract meaningful insights. Data science enables predictive maintenance by identifying patterns that signal equipment failure, optimizing operations through real-time analytics, and enhancing decision-making with actionable insights. It also supports anomaly detection to identify unusual behaviors and improve security. Overall, data science transforms raw IoT data into valuable information for better efficiency, safety, and innovation.