Error during plot the decision bounndry on a traning and test sets...

[Parth-107]

This is my code and error when I try to plot the decision bounndry on a traning and test set :-
plt.figure(figsize=(9, 6))
plt.subplot(1,2,1)#(rows, cols, 1st plot)
plt.title("Training Data")
****plot_decision_boundry(model_8,x_train,y_train)#Error on this line
plt.subplot(1,2,2)
plt.title("Testing Data")
plot_decison_boundry(model= model_8,x=x_test,y=y_test)#Error on this line as well
plt.show()

/usr/local/lib/python3.7/dist-packages/matplotlib/init.py in inner(ax, data, *args, **kwargs)
1563 def inner(ax, *args, data=None, **kwargs):
1564 if data is None:
-> 1565 return func(ax, *map(sanitize_sequence, args), **kwargs)
1566
1567 bound = new_sig.bind(ax, *args, **kwargs)

/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/deprecation.py in wrapper(*args, **kwargs)
356 f"%(removal)s. If any parameter follows {name!r}, they "
357 f"should be pass as keyword, not positionally.")
--> 358 return func(*args, **kwargs)
359
360 return wrapper

/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)
4401 self._parse_scatter_color_args(
4402 c, edgecolors, kwargs, x.size,
-> 4403 get_next_color_func=self._get_patches_for_fill.get_next_color)
4404
4405 if plotnonfinite and colors is None:

/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_axes.py in _parse_scatter_color_args(c, edgecolors, kwargs, xsize, get_next_color_func)
4240 except ValueError:
4241 if not valid_shape:
-> 4242 raise invalid_shape_exception(c.size, xsize)
4243 # Both the mapping and the RGBA conversion failed: pretty
4244 # severe failure => one may appreciate a verbose feedback.

ValueError: 'c' argument has 800 elements, which is inconsistent with 'x' and 'y' with size 1000.

This is my plot_decision_boundry:-
def plot_decision_boundry(model, x, y):
#define the axis boundries of the plot and create the meshgrid
x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1
y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1
xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
np.linspace(y_min, y_max, 100))
x_in = np.c_[xx.ravel(), yy.ravel()] #np.c_ will make all the values in single axis and ravel() function returns a contiguous flattened array
y_pred = model.predict(x_in)

if len(y_pred[0]) > 1:
print("Doing multiclass classification")
#if so then we have change the shape for that
y_pred = np.argmax(y_pred, axis=1).reshape(xx.shape)
else:
print("doing Binary classification")
y_pred = np.round(y_pred).reshape(xx.shape)

#plot the decision boundry
plt.contourf(xx, yy, y_pred, cmap= plt.cm.RdYlBu, alpha = 0.7)
plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap= plt.cm.RdYlBu)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())

[mrdbourke]

Hey Parth,

Thank you for sharing your code.

What have you tried so far?

It looks like there's a dimensional error for c.

You might want to make sure you're using the right x value for this line:

x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1
y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1

Think about what the input to the function is, x or X (lowercase or capital).

[Parth-107]

Wow, you are amazing Man..! It's working now. How you have developed this kind of programming skills, share with me too LOL.

Thank you so munch for helping me.

[mrdbourke]

Woohoo! Glad you fixed it!

Haha I've still got plenty to learn, all it takes is practice over time