The article discusses the importance of predictive accuracy in machine learning and focuses on the concepts of interpolation and extrapolation. Interpolation involves predicting values within the range of known data points, while extrapolation involves predicting values outside of this range. Both techniques are crucial for accurately predicting outcomes in various scenarios. The article provides a detailed explanation of how interpolation and extrapolation work, as well as the potential pitfalls and challenges associated with each method. It also discusses the importance of choosing the right model and parameters to achieve the best predictive accuracy. Overall, the article serves as a guide for understanding and implementing interpolation and extrapolation in machine learning for improved predictive accuracy.
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Source link: https://towardsdatascience.com/the-machine-learning-guide-for-predictive-accuracy-interpolation-and-extrapolation-45dd270ee871
#MachineLearning Guide: Interpolation, Extrapolation for Predictive Accuracy #DataScience
