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componentspcaanalysisprincipal
akD
akD

Reputation: 1257

When to use PCA(n_components=0.95) and when to use PCA(n_components=2), what is the difference between them?

For the Principal Component Analysis (PCA) model training

when to pass variance as PCA(n_components=0.95) and when to use PCA(n_components=2) with pipeline having Standardscaler for standardizes the feature values.

Upvotes: 0

Views: 136

Answers (1)

akD
akD

Reputation: 1257

pipeline = make_pipeline(
    StandardScaler(),
    PCA(n_components=0.95)  # Retain 95% of the variance
)



pipeline = make_pipeline(
    StandardScaler(),
    PCA(n_components=2)  # Reduce to exactly 2 dimensions
)

When to Use Each

Use n_components=0.95:

  • When you are dealing with datasets with high dimensionality and you want to reduce the number of features while retaining most of the information.
  • When preparing data for machine learning algorithms to improve efficiency and reduce overfitting.
  • When you need to understand the principal components that capture most of the variance in your data.

Use n_components=2:

  • When you need to visualize the data in 2 dimensions.
  • When the task requires a fixed number of dimensions, such as certain clustering algorithms or when creating 2D representations for human interpretation.

Upvotes: 0

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