You run the following code to train an MLPClassifier but encounter an error. What could be the issue?  Code :from sklearn.neural_network import MLPClassifier model = MLPClassifier(hidden_layer_sizes=(100,), max_iter=200) model.fit(X_train, y_train)Error: ValueError: could not convert string to float: 'setosa'  The target variable y_train contains string labels that need to be encoded.The input features X_train contain string values.The number of iterations is too low.The target variable X_train contains string labels that need to be encoded.

Identify which line has Error1. from sklearn.neural_network import MLPClassifier  2. model = MLPClassifier(hidden_layer_sizes=(100,), max_iter=200) 3. model.fit(X_train, y_train) 4. predictions = model.predict(X_test, y_test)  1 & 3234

Which of the following options will complete the missing code lines to:i) train the MLPClassifier,ii) predict the test set labels,iii) count the number of misclassified samples,iv) call the function to print the results.from sklearn.datasets import make_moonsfrom sklearn.model_selection import train_test_splitfrom sklearn.neural_network import MLPClassifierfrom sklearn.preprocessing import StandardScalerimport numpy as np# Generate a two-moon datasetX, y = make_moons(n_samples=1000, noise=0.2, random_state=42)# Split the dataset into training and test setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)# Scale the featuresscaler = StandardScaler()X_train_scaled = scaler.fit_transform(X_train)X_test_scaled = scaler.transform(X_test)# Initialise the MLPClassifier with one hidden layer with 10 neuronsmlp = MLPClassifier(hidden_layer_sizes=(10,), max_iter=1000, random_state=42)# [Your Code Here] - Train the MLPClassifier on the scaled training data# [Your Code Here] - Predict the labels for the scaled test data# [Your Code Here] - Print the number of misclassified samples in the test setmlp.fit(X_train_scaled, y_train)y_pred = mlp.predict(X_test_scaled)print(np.sum(y_test != y_pred))mlp.train(X_train_scaled, y_train)y_pred = mlp.classify(X_test_scaled)print((y_test - y_pred).count_nonzero())mlp.fit(X_train_scaled, y_train)y_pred = mlp.predict(X_test_scaled)misclassified = np.where(y_test != y_pred, 1, 0)print(misclassified.sum())mlp.train(X_train_scaled, y_train)y_pred = mlp.test(X_test_scaled)print(np.count_nonzero(y_test == y_pred))

Which method is used to train the MLP model in scikit-learn?  train()fit()learn()predict()

ValueError Traceback (most recent call last)Cell In[176], line 6 4 # Standardize features 5 scaler = StandardScaler()----> 6 X_train = scaler.fit_transform(X_train) 7 X_test = scaler.transform(X_test) 9 # Train Random Forest RegressorFile ~\anaconda3\lib\site-packages\sklearn\utils\_set_output.py:313, in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs) 311 @wraps(f) 312 def wrapped(self, X, *args, **kwargs):--> 313 data_to_wrap = f(self, X, *args, **kwargs) 314 if isinstance(data_to_wrap, tuple): 315 # only wrap the first output for cross decomposition 316 return_tuple = ( 317 _wrap_data_with_container(method, data_to_wrap[0], X, self), 318 *data_to_wrap[1:], 319 )File ~\anaconda3\lib\site-packages\sklearn\base.py:1098, in TransformerMixin.fit_transform(self, X, y, **fit_params) 1083 warnings.warn( 1084 ( 1085 f"This object ({self.__class__.__name__}) has a `transform`" (...) 1093 UserWarning, 1094 ) 1096 if y is None: 1097 # fit method of arity 1 (unsupervised transformation)-> 1098 return self.fit(X, **fit_params).transform(X) 1099 else: 1100 # fit method of arity 2 (supervised transformation) 1101 return self.fit(X, y, **fit_params).transform(X)File ~\anaconda3\lib\site-packages\sklearn\preprocessing\_data.py:878, in StandardScaler.fit(self, X, y, sample_weight) 876 # Reset internal state before fitting 877 self._reset()--> 878 return self.partial_fit(X, y, sample_weight)File ~\anaconda3\lib\site-packages\sklearn\base.py:1473, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs) 1466 estimator._validate_params() 1468 with config_context( 1469 skip_parameter_validation=( 1470 prefer_skip_nested_validation or global_skip_validation 1471 ) 1472 ):-> 1473 return fit_method(estimator, *args, **kwargs)File ~\anaconda3\lib\site-packages\sklearn\preprocessing\_data.py:914, in StandardScaler.partial_fit(self, X, y, sample_weight) 882 """Online computation of mean and std on X for later scaling. 883 884 All of X is processed as a single batch. This is intended for cases (...)

, you are given a list of items in a DataFrame as below.D = [‘A’,’B’,’C’,’D’,’E’,’AA’,’AB’]Now, you want to apply label encoding on this list for importing and transforming, using LabelEncoder.from sklearn.preprocessing import LabelEncoderle = LabelEncoder()What will be the output of the print statement below ?print le.fit_transform(D)array([0, 2, 3, 4, 5, 6, 1])array([0, 3, 4, 5, 6, 1, 2])array([0, 2, 3, 4, 5, 1, 6])Any of the above