WIP Add module on dimensionality reduction#876
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This was referenced May 5, 2026
| ax.bar_label(bars) | ||
| ax.set_xlim([0, 14]) | ||
| ax.set_yticks([1, 2], labels=["PC1", "PC2"]) | ||
| ax.set_xlabel("eigenvalues") |
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My suggestion would be to use explained variance in the plot as it the term eigenvalues is not used (yet):
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| ax.set_xlabel("eigenvalues") | |
| ax.set_xlabel("Explained variance") |
| # with each other. Two strongly correlated features will jointly define a | ||
| # direction with much higher variance than either one alone, and the explained | ||
| # variance ratios across components will still be very unequal. Scaling removes | ||
| # the unit bias; it does not make all directions equally important. |
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I would say that without scaling a magnitude bias is introduced more than a unit bias:
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| # the unit bias; it does not make all directions equally important. | |
| # the magnitude bias; it does not make all directions equally important. |
| # --- | ||
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| # %% [markdown] | ||
| # # Solution for Exercise M8.01 |
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| # # Solution for Exercise M8.01 | |
| # # Exercise M8.01 |
| # variance, keeps even more: all 900 components we computed pass it, meaning the | ||
| # true cutoff lies beyond what we measured. | ||
| # | ||
| # For text data, a common practice is to fix the number of components to be 100 |
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Out of curiousity; where do these values come from? Why not between 100 and 300?
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Adds module on PCA. Adds the wiki_news dataset, notebooks and exercises.
Probably a good idea to merge this after the clustering module in #836.
Note: This is still WIP. We are missing at least one quiz and the wrap-up quiz.