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.
<code>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
)
</code>
<code>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
)
</code>
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.