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We can run the LDA product instantly on the validation set and summarize the results inside a confusion matrix.
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Thanks for the tutorial. Even so, I have an issue about featurePlot purpose with plot = “density †choice. I couldn’t determine the that means of vertical axis you could try here in these plots for each attributes. Why the vertical axes have values which are better than 1 (in the case of density)
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Up coming we can get an notion of the distribution of each attribute, yet again just like the box and whisker plots, broken down by course value. In some cases histograms are great for this, but In this instance We'll use some likelihood density plots to give nice easy lines for every distribution.
# break up input and output x This provides us a Significantly clearer notion of the distribution from the input characteristics:
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