How to plot the decision boundary of different combinations of 2 features from 107 feature data set?
# like this but it is impossible to do like for 107 features. for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]): # We only take the two corresponding features X = iris.data[:, pair] y = iris.target`
Any suggestions for such an approach?
So that I can link it to my program:
X = array[:,[51,98]] X = StandardScaler().fit_transform(X) y = array[:,0] y = y.astype(int) # OneHot encoding" from sklearn.neighbors import KNeighborsClassifier` #k = 15 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=0) viz = DecisionViz( KNeighborsClassifier(10), title="Nearest Neighbors", features=['Feature One', 'Feature Two'], classes=['A', 'B'] ) viz.fit(X_train, y_train) viz.draw(X_test, y_test) print(viz.score(X_test, y_test)) viz.show(dpi = 300)
where X would be 2 out of 107 features and it would try every 2 combinations out of 107. Why would I do this, I wanted to see which 2 features were able to distinguish my 2 target classes.