Unable to calculate Model performance for Decision Tree Regressor - python-3.x

Question

Although my code run fine on repl and did giving me results but it miserably fails on the Katacoda testing environment.
I am attaching the repl file here for your review as well, which also contains the question which is commented just above the code I have written.
Kindly review and let me know what mistakes I am making here.
Repl Link
https://repl.it/repls/WarmRobustOolanguage
Also sharing code below
Commented is Question Instructions
#Import two modules sklearn.datasets, and #sklearn.model_selection.
#Import numpy and set random seed to 100.
#Load popular Boston dataset from sklearn.datasets module #and assign it to variable boston.
#Split boston.data into two sets names X_train and X_test. #Also, split boston.target into two sets Y_train and Y_test.
#Hint: Use train_test_split method from #sklearn.model_selection; set random_state to 30.
#Print the shape of X_train dataset.
#Print the shape of X_test dataset.
import sklearn.datasets as datasets
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
np.random.seed(100)
max_depth = range(2, 6)
boston = datasets.load_boston()
X_train, X_test, Y_train, Y_test = train_test_split(boston.data, boston.target, random_state=30)
print(X_train.shape)
print(X_test.shape)
#Import required module from sklearn.tree.
#Build a Decision tree Regressor model from X_train set and #Y_train labels, with default parameters. Name the model as #dt_reg.
#Evaluate the model accuracy on training data set and print #it's score.
#Evaluate the model accuracy on testing data set and print it's score.
#Predict the housing price for first two samples of X_test #set and print them.(Hint : Use predict() function)
dt_reg = DecisionTreeRegressor(random_state=1)
dt_reg = dt_reg.fit(X_train, Y_train)
print('Accuracy of Train Data :', cross_val_score(dt_reg, X_train,Y_train, cv=10 ))
print('Accuracy of Test Data :', cross_val_score(dt_reg, X_test,Y_test, cv=10 ))
predicted = dt_reg.predict(X_test[:2])
print(predicted)
#Fit multiple Decision tree regressors on X_train data and #Y_train labels with max_depth parameter value changing from #2 to 5.
#Evaluate each model accuracy on testing data set.
#Hint: Make use of for loop
#Print the max_depth value of the model with highest accuracy.
dt_reg = DecisionTreeRegressor()
random_grid = {'max_depth': max_depth}
dt_random = RandomizedSearchCV(estimator = dt_reg, param_distributions = random_grid,
n_iter = 90, cv = 3, verbose=2, random_state=42, n_jobs = -1)
dt_random.fit(X_train, Y_train)
dt_random.best_params_
def evaluate(model, test_features, test_labels):
predictions = model.predict(test_features)
errors = abs(predictions - test_labels)
mape = 100 * np.mean(errors / test_labels)
accuracy = 100 - mape
print('Model Performance')
print('Average Error: {:0.4f} degrees.'.format(np.mean(errors)))
print('Accuracy = {:0.2f}%.'.format(accuracy))
return accuracy
best_random = dt_random.best_estimator_
random_accuracy = evaluate(best_random, X_test,Y_test)
print("Accuracy Scores of the Model ",random_accuracy)
best_parameters = (dt_random.best_params_['max_depth']);
print(best_parameters)

Answer 1

The question is asking for default values. Try to remove random_state=1
Current Line:
dt_reg = DecisionTreeRegressor(random_state=1)
Update Line:
dt_reg = DecisionTreeRegressor()
I think it should Work!!!

Answer 2

# ================================================================================
# Machine Learning Using Scikit-Learn | 3 | Decision Trees ================================================================================
import sklearn.datasets as datasets
import sklearn.model_selection as model_selection
import numpy as np
from sklearn.tree import DecisionTreeRegressor
np.random.seed(100)
# Load popular Boston dataset from sklearn.datasets module and assign it to variable boston.
boston = datasets.load_boston()
# print(boston)
# Split boston.data into two sets names X_train and X_test. Also, split boston.target into two sets Y_train and Y_test
X_train, X_test, Y_train, Y_test = model_selection.train_test_split(boston.data, boston.target, random_state=30)
# Print the shape of X_train dataset
print(X_train.shape)
# Print the shape of X_test dataset.
print(X_test.shape)
# Build a Decision tree Regressor model from X_train set and Y_train labels, with default parameters. Name the model as dt_reg
dt_Regressor = DecisionTreeRegressor()
dt_reg = dt_Regressor.fit(X_train, Y_train)
print(dt_reg.score(X_train,Y_train))
print(dt_reg.score(X_test,Y_test))
predicted = dt_reg.predict(X_test[:2])
print(predicted)
# Get the max depth
maxdepth = 2
maxscore = 0
for x in range(2, 6):
dt_Regressor = DecisionTreeRegressor(max_depth=x)
dt_reg = dt_Regressor.fit(X_train, Y_train)
score = dt_reg.score(X_test, Y_test)
if(maxscore < score):
maxdepth = x
maxscore = score
print(maxdepth)