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Decision Tree, KNN, & Random Forest (Methods that are suitable for overlapping data)
This statement is false. All those methods are good when the decision surface (separating surface) has a highly nonlinear form. They act as a non-parametric local approximation - all parameters are not in fact parameters of the decision function but are meta parameters ...
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Your data is multidimensional, it is possible that any two dimensional projection overlaps while still existing an hyperplane on the original dimensionality that separates the two classes well
Say for instance you have 3 data points from 2 labels in 2d that are linearly separable
X:(0,-1) O:(1,2) X:(4,3)
X
O
X
In the x axis they look ...
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This seems to be a bar chart made using Google sheets, see also this chart which was made with Google sheets:
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The variations between lines in the image you have provided are usually set using color and line style properties in a programmatic plotting library (e.g. gnuplot, matplotlib in Python, etc).
Specifically how to control color and style varies from program to program, but an example showing a Matplotlib plot using the Seaborn styling package is similar to the ...
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You can also use plotly's for lines chart, which provide a more interactive feeling of your lines (e.g hovering over a line will display the values at this specific point). It is highly customizable so you can play around with it. In the documentation, there is an example specific to your case.
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It's really difficult to know with what tool they do that. but I will give my chances to R or Excel. That overlapping is automatic perform with excel just selecting the data this is the instruction-: Go to the insert section: char category and choose the 2-D Column, Style 7, Layout 16.
also you can perform a "similar" plot with ggplot and the ...
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Detailed information on the dataset is available in the "Data Description" text. Each number is indeed a separate case or patient if you will. Think of the numbers given as the patient ID numbers. So patients numbered 0, 2, 3, 5 etc are in the training dataset, patients numbered 1, 13, 15, 27 etc are in the public test - or validation dataset. ...
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