# Import packages and functions

import numpy as np

import pandas as pd

from sklearn import metrics

from sklearn.model$\_$selection import train$\_$test$\_$split

from sklearn.neural$\_$network import MLPClassifier

# Load the breast cancer dataset

cancer $=$ pd$.$read$\_$csv$(\text{'}$WisconsinBreastCancerDatabase$.$csv$\text{'})$

# Select input and output features

X $=$ cancer.drop$($columns$=[\text{'}$ID$\text{'},$ 'Diagnosis', 'Symmetry se$\text{'}])$

y $=$ cancer$[[\text{'}$Diagnosis$\text{'}]]$

# Split the data into train and test datasets

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($X$,$ y$,$ test$\_$size$=0\mathrm{.}3,$ random$\_$state$=123)$

# Initialize a multilayer perceptron classifier with default parameters and

# random$\_$state set to $78$

modelPerceptron $=$ MLPClassifier$($hidden$\_$layer$\_$sizes$=(50,),$ activation$=$'relu', solver$=$'adam', alpha$=0\mathrm{.}01,$ random$\_$state$=78,$ max$\_$iter$=2000)$

# Fit multilayer perceptron classifier to X$\_$train and y$\_$train

modelPerceptron.fit$($X$\_$train, y$\_$train.values.ravel$())$

# Print R$-$squared score for training data

trainScore $=$ modelPerceptron.score$($X$\_$train, y$\_$train$)$

print$($trainScore$)$

# Print R$-$squared score for testing data

testScore $=$ modelPerceptron.score$($X$\_$test, y$\_$test$)$

modelPerceptron $=$ MLPClassifier$($random$\_$state$=78)$ initializes a multilayer perceptron classifier. The $.$fit$()$ method applies the model to the given input and output features. The $.$score$()$ method returns the R$-$squared score.

Not all tests passed.

Output differs. See highlights below.

Your output

$0\mathrm{.}89047619047619040\mathrm{.}9222222222222223$

Expected output

$0\mathrm{.}93333333333333330\mathrm{.}9222222222222223$

what am I doing wrong?