课程: Machine Learning with Python: Foundations

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Reducing the dimensionality of your data

Reducing the dimensionality of your data - Python教程

课程: Machine Learning with Python: Foundations

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Reducing the dimensionality of your data

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- [Instructor] As we prepare our data for machine learning, we sometimes have to reduce the size or complexity of our data. There are several ways to do this. One approach is sampling, which helps us reduce the number of rows in our data. Another approach is dimensionality reduction. As the name suggests, dimensionality reduction is simply the reduction in the number of features or dimensions in a dataset. Dimensionality reduction is an important step in the machine learning process because it helps reduce the time and storage required to process data. It improves data visualization and model interpretability. It also helps avoid the curse of dimensionality. The curse of dimensionality is a phenomenon in machine learning that describes the eventual reduction in the performance of a model as a dimensionality of the training data increases. Specifically, as we increase the number of features that we use to build a model,…

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