some small understandability improvements to the README #79
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…ders where I will add tables to the markdown
vc1492a
reviewed
Jul 7, 2025
| according to the distribution of each cluster. Which approach is suitable depends on the use case. | ||
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| **NOTE**: Data was not normalized in this example, but it's probably a good idea to do so in practice. | ||
| - Why? |
Owner
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The main reason is to note give extra weight to a particular column of data. Data normalization ensures that all features in a dataset are on a similar scale, preventing features with larger values from disproportionately influencing algorithms and improving model performance.
| print(scores) | ||
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| ``` | ||
| -- I'll insert a table here |
| print(scores) | ||
| ``` | ||
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| -- I'll insert a table of the scores here |
| m = loop.LocalOutlierProbability(distance_matrix=d, neighbor_matrix=idx, n_neighbors=n_neighbors+1).fit() | ||
| scores = m.local_outlier_probabilities | ||
| ``` | ||
| -- insert a table of the results |
Owner
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A full table of the values may be too large, but showing a truncated view would be helpful.
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and placeholders where I will add tables to the markdown