{"id":8050,"date":"2021-01-11T03:55:56","date_gmt":"2021-01-11T03:55:56","guid":{"rendered":"https:\/\/wealthrevelation.com\/data-science\/2021\/01\/11\/how-principal-component-analysis-pca-works\/"},"modified":"2021-01-11T03:55:56","modified_gmt":"2021-01-11T03:55:56","slug":"how-principal-component-analysis-pca-works","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2021\/01\/11\/how-principal-component-analysis-pca-works\/","title":{"rendered":"How Principal Component Analysis, PCA Works"},"content":{"rendered":"
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\"Principal\"Principal<\/span><\/div>\n
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Whoever tried to build machine learning models<\/strong><\/a> with many features would already know the glims about the concept of principal component analysis. In short PCA<\/strong>.<\/p>\n

The inclusion of more features in the implementation of machine learning algorithms models might lead to worsening performance issues. The increase in the number of features will not always improve classification accuracy<\/strong><\/a>.\u00a0<\/p>\n

When enough features are not present<\/strong> in the data, the model is likely to underfit<\/strong>, and when data contains too many<\/strong> features, it is expected to overfit or underfit. This phenomenon is known as the curse of dimensionality<\/strong>.\u00a0<\/p>\n<\/div>\n

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Learn how the popular dimension reduction technique PCA (principal component analysis) works and learn the implementation in python. #pca #datascience #machinelearning #python\u00a0<\/p>\n<\/p><\/div>\n

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Therefore, we apply dimensionality reduction by selecting the optimal set<\/strong> of lower dimensionality features in order to improve classification accuracy<\/strong><\/a>.<\/p>\n

Following are the techniques to perform the dimensionality reduction:<\/p>\n

If you are not sure about the PCA (principal component analysis )and the need for dimensionality reduction, don’t worry.<\/p>\n

You are in the right place. In this article, we are going to cover everything.<\/p>\n

Before we dive further, below are the topics you are going to learn in this article. Only if you read the complete article \ud83d\ude42<\/p>\n<\/div>\n

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Let\u2019s start the discussion with the curse of dimensionality and its impact on building machine learning models<\/strong><\/a>.<\/p>\n

Curse of Dimensionality<\/h2>\n

Curse of Dimensionality can be defined as:<\/p>\n

The set of problems that arise when we work with high-dimensional data.<\/p><\/blockquote>\n

The dimension of a dataset is directly related to the number of features<\/strong> that are present in a dataset.\u00a0<\/p>\n

High-dimensional data can be defined as a dataset having a large number of attributes, generally of the order of a hundred or more.<\/p>\n<\/div>\n

\"Curse\"Curse<\/span><\/div>\n
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The difficulties that arise with high dimensional data arise during analysis and visualization of the data to identify patterns. Others manifest when we train the machine learning models.\u00a0<\/p>\n

The curse of dimensionality can be defined in other words as:<\/p>\n

The rise of difficulties due to the presence of high dimensional data when we train the machine learning models.<\/p><\/blockquote>\n

The popular aspects of the curse of dimensionality are <\/p>\n