SVM plot for a non-linear decision boundary

Question

I am trying to plot SVM decision boundary which separates two classes, cancerous and non-cancerous. However, it's displaying a plot which is far from what I wanted. I wanted it to look like this:

or anything that shows the points are scattered. Here's my code:

import numpy as np
import pandas as pd
from sklearn import svm
from mlxtend.plotting import plot_decision_regions
import matplotlib.pyplot as plt

autism = pd.read_csv('predictions.csv')


# Fit Support Vector Machine Classifier
X = autism[['TARGET','Predictions']]
y = autism['Predictions']

clf = svm.SVC(C=1.0, kernel='rbf', gamma=0.8)
clf.fit(X.values, y.values) 

# Plot Decision Region using mlxtend's awesome plotting function
plot_decision_regions(X=X.values, 
                      y=y.values,
                      clf=clf, 
                      legend=2)

# Update plot object with X/Y axis labels and Figure Title
plt.xlabel(X.columns[0], size=14)
plt.ylabel(X.columns[1], size=14)
plt.title('SVM Decision Region Boundary', size=16)
plt.show()

But I got a weird looking plot:

You can find the csv file here predictions.csv

Answer 1

You sound a little confused...

Your predictions.csv looks like:

TARGET  Predictions
     1  0
     0  0
     0  0
     0  0

and, as I guess the column names imply, it contains the ground truth ( TARGET ) and the Predictions of some (?) model already run.

Given that, what you are doing in your posted code makes absolutely no sense at all: you are using both these columns as features in your X in order to predict your y , which is... exactly one of these same columns ( Predictions ), already contained in your X ...

Your plot looks "strange" simply because what you have plotted are not your data points, and the X and y data you show here are not the data that should be used for fitting your classifier.

I am further puzzled because, in your linked repo, you have indeed the correct procedure in your script:

autism = pd.read_csv('10-features-uns.csv')

x = autism.drop(['TARGET'], axis = 1)  
y = autism['TARGET']
x_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.30, random_state=1)

ie reading your features and labels from 10-features-uns.csv , and certainly not from predictions.csv , as you are inexplicably trying to do here...