Comments (1)
The first component seems to explains 96% of all variation in your data (thats a lot). The second 4%. Thus all features that are horizontal-ish have the most weight. Did you normalize the data? Maybe that can help. Alternatively, Have you checked whether you have highly correlated features? You can try to remove the multicollinearity at start and then re-run the analysis. You can also limit the number of loadings but that is only for visualization purposes.
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Related Issues (20)
- title field in CITATION.cff contains description HOT 4
- Logistic PCA and PPMI-based methods? HOT 3
- Unable turn off the default plot title of `biplot()`. HOT 2
- NIPALS decomposition method HOT 1
- Feature request: use helper function approach for plotting HOT 6
- Unable to set colors for labels HOT 5
- Circular import issue HOT 2
- customizing marker shape HOT 4
- How to remove text labels from scatterplots? HOT 3
- Issue with biplot labels colour after new PCA installation using pip HOT 1
- Varimax and Promax usage, an alternative to ICA?
- What is the purpose of calling fig.set_visible(visible)? HOT 6
- Optionally suppress the center-annotations in scatter plots HOT 3
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- model.biplot() error when pd.options.mode.copy_on_write = True HOT 3
- New pca version not producing expected graphs HOT 3
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- Typo in axis label HOT 1
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- Transform with update_outlier_params=False will still change Hotelling T2 outlier results on the fit data HOT 1
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