attached you see a decision boundary graph of a classification algorithm made in ggplot(from this great article)I was curious about if it is possible to replicate such graphs in highcharter, or more specifically: Is it possible to create custom boundary lines / areas for different classification algorithms? I would highly appreciate an example and thank you for your support.
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I am trying to rewrite the code of this paper: https://doi.org/10.1038/s42003-020-0837-0
I have written the code step-by-step based on the instructions mentioned in the methods section. But after clustering, for plotting the clusters by dimplot, I receive a dissimilar plot compared to the same plot in the paper.
I wonder what is the problem? I have tailored every parameter to receive the same plot but it hasn't worked yet.
Graph of the paper
My graph
Please help me to solve this issue.
I have generated a community network using iGraph and qGraph to determine the relatedness between individual's genetic codes above a threshold edge value. To further analyze this data, I used cluster_walktrap and then graphed this along with the network. I am wondering if there is a way label the individual communities, as well as plot a single community from the batch. I can provide extra details if need be. Thanks
Do you mean something using components, e.g.,
sapply(components(g), plot)
I got some figures after I did decision tree model using part library.
This figures shows fundamental function of part library.
In these figures, I understand all excepts fourth kind of figure.
This figure don't have any powful feature. It does not show any information. How can I understand this figure?
You are right. The fourth panel shows the visualization of a tree without any splits. Thus, in this case none of the available split variables improved the cost-complexity criterion of rpart and hence only the root node remains.
The visualization using the partykit package employs stacked bar plots for every terminal node in the trees (the default visualization for binary classification in the package). Thus, there is only a single stacked bar in case of a root node only.
I have a fair amount of experience analyzing RNA-Seq data, but I am looking for new ways to visualize the data. I typically use heat maps and volcano plots, but I'd like to make this plot which is from this paper. I can make this type of plot with rlog transformed data before doing DEG analysis, but I want to color dots based on statistically significant expression differences.
I've search online and have not been able to find a good way to create this plot. Thanks in advance for any advice.
This question is more about bioinformatics so maybe it is better you can post it on biostar.
In any case, maybe you can draw a scatter plot with the package "ggscatter" or "ggplot2" and colour the statistically significant gene with an if else statement.
Please, provide sample of your data.
I am using scikit-learn to understand Support Vector Machines(SVM). I want to plot the decision boundary computed by SVM. The SVM uses 3 features. So the decision boundary must be drawn in 3D space. Is this possible using scikit-learn?
I could find only 2D plots of SVM decision boundary at the official website.
However I found links on stackoverflow which show this is possible using matlab and r. Is there a way in which I can achieve the same using scikit-learn?
You don't use scikit-learn to plot things in Python. You have to use another package, and if you check scikit-learn's examples, you will see they use matplotlib. You can create 3D plots with matplotlib, here is the tutorial. The usage is basically the same than in 2D, except you add an argument for the third dimension.
Another popular package you could use is mayavi, which was conceived especially for 3D plotting.