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assignments:assignment3 [2016/09/19 10:09] asa |
assignments:assignment3 [2016/09/19 12:12] asa [Part 3] |
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We will explore the relationship between the magnitude of weight vector components and their relevance to the classification task in several ways. | We will explore the relationship between the magnitude of weight vector components and their relevance to the classification task in several ways. | ||
Each feature is associated with a component of the weight vector. It can also be associated with the correlation of that feature with the vector of labels. | Each feature is associated with a component of the weight vector. It can also be associated with the correlation of that feature with the vector of labels. | ||
- | Create a scatter plot of the weight vector component against the [[https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient | Pearson correlation coefficient]] of a feature against the labels (again, you can use the [[http://docs.scipy.org/doc/numpy/reference/routines.statistics.html | Numpy statistics module]] to compute it). | + | As we discussed in class, the magnitude of the weight vector can give an indication of feature relevance; another measure of relevance of a feature is its correlation with the labels. To compare the two, |
+ | create a scatter plot of weight vector components against the [[https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient | Pearson correlation coefficient]] of the corresponding feature with the labels (again, you can use the [[http://docs.scipy.org/doc/numpy/reference/routines.statistics.html | Numpy statistics module]] to compute it). | ||
What can you conclude from this plot? | What can you conclude from this plot? | ||
The paper ranks features according to their importance using a different approach. Compare your results with what they obtain. | The paper ranks features according to their importance using a different approach. Compare your results with what they obtain. |