How to plot decision boundaries for Random Forest classifier - random-forest

Question

How to go about plotting the decision boundaries for a Random Forest analysis with 10 classes?
I get the error:
ValueError: X has 2 features, but RandomForestClassifier is expecting
240 features as input.
Can you help me get the decision boundaries for the 10 classes if possible? Thanks for your time!
Here is my code:
from sklearn.datasets import make_classification
import seaborn as sns
import numpy as np
from matplotlib import pyplot as plt
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
f, (ax1,ax2) = plt.subplots(nrows=1, ncols=2,figsize=(20,8))
# Generate noisy Data
num_trainsamples = 500
num_testsamples = 50
X_train,y_train = make_classification(n_samples=num_trainsamples,
n_features=240,
n_informative=9,
n_redundant=0,
n_repeated=0,
n_classes=10,
n_clusters_per_class=1,
class_sep=9,
flip_y=0.2,
#weights=[0.5,0.5],
random_state=17)
X_test,y_test = make_classification(n_samples=50,
n_features=num_testsamples,
n_informative=9,
n_redundant=0,
n_repeated=0,
n_classes=10,
n_clusters_per_class=1,
class_sep=10,
flip_y=0.2,
#weights=[0.5,0.5],
random_state=17)
model = RandomForestClassifier()
parameter_space = {
'n_estimators': [10,50,100],
'criterion': ['gini', 'entropy'],
'max_depth': np.linspace(10,50,11),
}
clf = GridSearchCV(model, parameter_space, cv = 5, scoring = "accuracy", verbose = True) # model
my_model = clf.fit(X_train, y_train)
# define bounds of the domain
min1, max1 = X_train[:, 0].min()-1, X_train[:, 0].max()+1
min2, max2 = X_train[:, 1].min()-1, X_train[:, 1].max()+1
# define the x and y scale
x1grid = np.arange(min1, max1, 0.1)
x2grid = np.arange(min2, max2, 0.1)
# create all of the lines and rows of the grid
xx, yy = np.meshgrid(x1grid, x2grid)
# flatten each grid to a vector
r1, r2 = xx.flatten(), yy.flatten()
r1, r2 = r1.reshape((len(r1), 1)), r2.reshape((len(r2), 1))
# horizontal stack vectors to create x1,x2 input for the model
grid = np.hstack((r1,r2))
yhat = clf.predict(grid)
# reshape the predictions back into a grid
zz = yhat.reshape(xx.shape)
# plot the grid of x, y and z values as a surface
plt.contourf(xx, yy, zz, cmap='Paired')
# create scatter plot for samples from each class
for class_value in range(2):
# get row indexes for samples with this class
row_ix = np.where(y == class_value)
# create scatter of these samples
plt.scatter(X_train[row_ix, 0], X_train[row_ix, 1], cmap='Paired')