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The explanation follows from understanding what a ROC curve is made of. First, a reminder: a ROC curve represents the performance of a soft binary classifier, i.e. a classifier which predicts a numerical value (usually a probability) which represents the likelihood of the instance to be positive. The points of a ROC curves correspond to what happens when ...

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This is a chord diagram. It is usuallly generated from a square matrix like the one you have. This can be done with specific libraries at least in R (circlize package) and Python (plotly). It's also possible to do it with d3.js but apparently not so easily. General advice: I find the "X Graph Gallery" websites quite convenient for exploring ...

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If you restructure your data a bit it is relatively straight forward: import matplotlib.pyplot as plt data = { "LR": [0.6, 0.7, 0.8, 0.7], "SVM": [0.7, 0.6, 0.8, 0.5], "Linear SVC": [0.8, 0.5, 0.7, 0.6], "ADABOOST": [0.7, 0.8, 0.6, 0.7], "DT": [0.6, 0.8, 0.5, 0.7] } subsets = [3, 5, 10, ...

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Try making these modifications to your function, it might look better. import matplotlib.pyplot as plt # optional but I like this style # plt.style.use("seaborn-whitegrid") def plot(results,names, score): # boxplot algorithm comparison fig = plt.figure() fig.suptitle(score) ax = fig.add_subplot(111) ax.plot(results, label = ...

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You will have to study and understand Matplotlib and its tweaks. This code will do the basic work. You can extend it. Also, please go through the references that are added at the end. import matplotlib.pyplot as plt data = {'AUC':{'RF':[0.7,0.2,0.5,0.9,0.4], 'LR':[0.9,0.25,0.35,0.99,0.55], 'SVM':[0.3,0.5,0.8,0.6,0.7] } } x = ['S1','S2','S3','S4','S5'] plt....

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Based on your explanation, $z$ is not a parameter so your function can be simplified like this: $$f(x,y,a) = x*y*z(x)*a\text{, where } x,y \in \mathbb{Q}\cap[0,1000], a\in\{2,3\}$$ Note that $z(x)$ is a piecewise function. Also $a$ can have only two values so $f$ can be divided into $f_2$ and $f_3$, each of these having only two arguments so it simplifies ...

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You can use the plt.scatter() and plt.subplots() to achieve this as follows: import matplotlib.pyplot as plt from sklearn.datasets import make_blobs data = make_blobs(n_samples=200, n_features=8, centers=6, cluster_std=1.8,random_state=101) fig, ax = plt.subplots(nrows=2, ncols=2,figsize=(10,10)) from sklearn.cluster import ...

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A tip: Dont A trick:Dont The reason? Machine learning scientific methodology is based on cross-validation. Almost all papers (and i put the almost because of yes) select everything based on cross-validation and not in previous knowledge. Xgboost is particularly more complicated because it has a lot of math involved. For a simpler case, lets say that you have ...

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