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May I know how to modify my Python programming so that can obtain the accuracy vs number of features as refer to the attached image file -

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
# load the data
iris = datasets.load_iris()
# get the features and labels from the data
x = iris.data
y = iris.target
# split the data into training and test data
X_train, X_test, y_train, y_test = train_test_split(x, y,test_size=0.7, random_state=0) 
# standardise the data
sc = StandardScaler()
X_train_std = sc.fit_transform(X_train)
X_test_std = sc.fit_transform(X_test)
# choose algorithm and set the hyperparameters
forest = RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=1)
# train the model
forest.fit(X_train_std, y_train)
# make the prediction using the model
y_pred = forest.predict(X_test_std)

A = []
C1 = [forest]
for i in range(len(C)):
 forest = RandomForestClassifier(C=C1[i], random_state=0)
 forest.fit(X_train_std,y_train)
 y_pred = forest.predict(X_test_std)
 A.append(accuracy_score(y_test,y_pred))

import matplotlib.pyplot as plt
plt.plot(C1, A)
plt.yticks(np.arange(0.90, 0.95, 0.01))
plt.xlabel('Number of features')
plt.ylabel('Accuracy')
plt.title('RansomForest')
plt.show()

The error message is -

runfile('C:/Users/HSIPL/Desktop/Homework 7 Solution draft.py', wdir='C:/Users/HSIPL/Desktop')
Traceback (most recent call last):

 File "<ipython-input-10-f06d5471b604>", line 1, in <module>
 runfile('C:/Users/HSIPL/Desktop/Homework 7 Solution draft.py', wdir='C:/Users/HSIPL/Desktop')

 File "C:UsersHSIPLAnaconda3libsite-packagesspyder_kernelscustomizespydercustomize.py", line 668, in runfile
 execfile(filename, namespace)

 File "C:UsersHSIPLAnaconda3libsite-packagesspyder_kernelscustomizespydercustomize.py", line 108, in execfile
 exec(compile(f.read(), filename, 'exec'), namespace)

 File "C:/Users/HSIPL/Desktop/Homework 7 Solution draft.py", line 28, in <module>
 forest = RandomForestClassifier(C=C1[i], random_state=0)

TypeError: __init__() got an unexpected keyword argument 'C'

Please see the attached image file -

Please help so that I can improve my computing skills

This is your homework, other people should not do it for you. Instead you need to learn how to interpret the information you got.

Your error message tells you that the issue is:

TypeError: __init__() got an unexpected keyword argument 'C'

So something is not expecting to have C as an argument. But the message also tell your which line the issue is with:

forest = RandomForestClassifier(C=C1[i], random_state=0)

This should hopefully identify which C gives us issues. If we check the sklearn documentation we can find which argument the classifier can input in it's init:

RandomForestClassifier(n_estimators=’warn’, criterion=’gini’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None) 

Using this information you should be able to get past this error message and be able to continue doing your homework on your own.

Also, the you probably got this error from copy and pasting code from an assignment where you used a SVM which does use C as an input. Copy pasting can easily make things harder when you are learning since the errors you get are more random.