The principal component analysis is a statistical method that allows you to summarise information into smaller sets that may be displayed and studied more readily. Large datasets are becoming more prevalent, yet they might be challenging to analyze. PCA is a technique for decreasing the dimensionality of datasets while enhancing interpretability and avoiding information loss. In the disciplines of statistics and data science, principal component analysis (PCA) is a crucial method to grasp. Let’s look at the advantages & disadvantages of Principal Component Analysis:
Advantages of Principal Component Analysis
Correlated features are removed
Running algorithms on large datasets with all of the features will lower the speed of your method and make it difficult to show the many characteristics in any type of graph. Finding connections in hundreds of characteristics manually is virtually difficult, tedious, and time-consuming because principal components are independent of one another, and associated characteristics are removed.
Enhances the performance of the algorithm.
With so many characteristics, your algorithm’s performance will surge greatly. The principal component analysis is a popular method for speeding up your machine Learning algorithm by removing associated variables that don’t help decision-making. With fewer features, the training time of the algorithms decreases considerably. So, if the input dimensions are too large, utilizing PCA to speed up the method is a viable option.
PCA converts high-dimensional data to low-dimensional data that can be readily viewed. PCA produces large variance, which helps visualization. PCA is based on linear algebra, which is computationally simple for computers to solve. It accelerates other machine learning methods, allowing them to converge quicker when trained on main components rather than the original dataset.
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Disadvantages of Principal Component Analysis
The major components are difficult to comprehend.
After applying principal component analysis to the dataset, your original features will be transformed into Principal Components. Original features are more legible and interpretable than Principal Components. Even the most basic invariance could not be caught by the PCA unless the training data clearly stated it. For example, after computing the main components, it is difficult to determine which characteristics in the dataset are the most significant.
Data normalization is required.
Before applying principal component analysis, you must normalize your data; otherwise, PCA will be difficult to discover optimal principal components. The scale has an effect on PCA, thus you must scale the features in your data before using PCA. As a result, principal components will be skewed toward characteristics with large variance, resulting in incorrect findings.
Loss of information.
PCA accounts for the greatest amount of variation across data characteristics. If the number of Principal Components is not carefully chosen, it may miss certain information in contrast to the real list of characteristics. Although dimensionality reduction is beneficial, it has a cost. Loss of information is an inevitable component of principal component analysis. Managing the trade-off between dimensionality reduction and information loss is, regrettably, an unavoidable tradeoff when employing PCA.
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