
Thermal Expansion
Thermal expansion is the tendency of matter to change its shape, area, and volume in response to a change in temperature. In data science, thermal expansion can be observed in the expansion of data sets due to the addition of new data points. As the size of the data set increases, the computational resources required to process the data also increase. This can lead to scalability issues, where the algorithms and models that were previously effective become inefficient. To address this, techniques such as data compression, feature selection, and dimensionality reduction can be used to reduce the size of the data set without losing important information.
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