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Scatter plot and box plots are the most preferred for visualizing outliers.
Parallel plots can also be utilized for detecting outliers. For large datasets it can be bit confusing, highlighting outliers comes in handy then.
parallel plot case study
outliers in parallel plot
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For x-axis:
No one can see 10K points on a single plot, therefore
give the user the opportunity to display desired range
How is it possible? For example,
user DataRangeSlider from bokeh widgets
For y-axis:
if your largest and smallest values are intrinsic to your system and you want to display them as they are, use logarithmic scale
if your largest and ...
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In my opinion I think it'll depend on why you want to visualize it / what you're trying to analyze. If it's appropriate, you might try something like a moving average to smooth out the sharp value changes.
I got this visualization from this blog post.
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I believe you are looking to work along with the missing values in particular column(X) where column(W,Y,Z) have important values in these rows and can't discard or do imputation, especially for plotting them visually.
Yes its possible, considering:
When you only plan to plot other columns(W,Y,Z excluding column X) to view them visually
When you only plan ...
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If your predictors have nothing to do with the outcome, you should not be able to build a model that works out-of-sample. This is a feature, not a bug, of machine learning. For instance, do you consider what time I set my alarm in the morning to be predictive whether or not you have cereal for breakfast?
Features can, however, have just a small relationship ...
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I found a another style of plot that might be interesting in this case: a boxplot.
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Following your comment, I'll detail here (too long for comments basically)
Acuracy may not be a good way to measure your model's performance. Imagine a problem with 99 '0' and 1 '1'. A model always gessing '0' will have 99% accuracy, and is useless, since you want to detect the '1'. A model giving you 10 '1' including the real one is way better, and have a ...
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The most intuitive way of visualizing your cluster results would be by using a linear projection like PCA.
In this way you can visualize for example the first 3 components and assign a color to each point according to cluster_id
Also important, you should in this case check the explained_variance as measure of how reliable the projection is, since you are ...
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