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This article mainly introduces the example analysis of feature dimensionality reduction in Python, which has a certain reference value, and interested friends can refer to it. I hope you will gain a lot after reading this article.

Description

1. PCA is the most classical and practical dimensionality reduction technology, especially in auxiliary pattern recognition.

2. It is used to reduce the dimensions of the dataset while maintaining the feature of the greatest contribution to the variance in the dataset.

Keep the low-order principal components and ignore the high-order components, the low-order components can often retain the most important part of the data.

Example

From sklearn.feature_selection import VarianceThreshold # feature selection VarianceThreshold deletes features with low variance (delete features with little difference) var = VarianceThreshold (threshold=1.0) # deletes features with a variance less than or equal to 1.0. Default threshold=0.0data = var.fit_transform ([[0,2,0,3], [0,1,4,3], [0,1,1,3]]) print (data)''[[0] [4] [1]''Thank you for reading this article carefully. I hope the article "sample Analysis of feature Dimension reduction in Python" shared by the editor will be helpful to you. At the same time, I hope you will support it. Pay attention to the industry information channel, more related knowledge is waiting for you to learn!

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