Train a decision tree with the following specifications:Using our previously encoded dataset, split the data into dependent and independent variables using all the features except for Standard_yield and Field_ID as independent variables.Split the data into training and testing data.Use the DecisionTreeRegressor to fit a model using a max_depth' of 2 and a random_state` of 42.Using the trained Decision Tree Regressor model, make a prediction for y given the following x-values:[864.66138, -8.12890218821531, -8.311822719284072, 16.274624300000003, 1237.7200000000003, -3.4100000000000006, 36.410000000000004, 16.5,0.682, 6.7863323423108195, 0.09379352739936421, 1.4300000000000002, 0.8264890400277934,0.0,0.0,0.0,0.0,0.0,0.0,1.1,0.0,0.0,1.1,0.0, 0.0,0.0,0.0,0.0,0.0]What is the value of the predicted y?Options0.32500770.66543770.484944140.8050340

# We instantiat the tree and specity the depth parameterclf=tree.DecisionTreeClassifier(max_depth=4)​# We fit the model using the training dataclf.fit(X_train,y_train)​clf---------------------------------------------------------------------------ValueError Traceback (most recent call last)Cell In[5], line 5 2 clf=tree.DecisionTreeClassifier(max_depth=4) 4 # We fit the model using the training data----> 5 clf.fit(X_train,y_train) 7 clfFile ~/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs) 1144 estimator._validate_params() 1146 with config_context( 1147 skip_parameter_validation=( 1148 prefer_skip_nested_validation or global_skip_validation 1149 ) 1150 ):-> 1151 return fit_method(estimator, *args, **kwargs)File ~/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py:959, in DecisionTreeClassifier.fit(self, X, y, sample_weight, check_input) 928 @_fit_context(prefer_skip_nested_validation=True) 929 def fit(self, X, y, sample_weight=None, check_input=True): 930 """Build a decision tree classifier from the training set (X, y). 931 932 Parameters (...) 956 Fitted estimator. 957 """--> 959 super()._fit( 960 X, 961 y, 962 sample_weight=sample_weight, 963 check_input=check_input, 964 ) 965 return selfFile ~/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py:366, in BaseDecisionTree._fit(self, X, y, sample_weight, check_input, missing_values_in_feature_mask) 363 max_leaf_nodes = -1 if self.max_leaf_nodes is None else self.max_leaf_nodes 365 if len(y) != n_samples:--> 366 raise ValueError( 367 "Number of labels=%d does not match number of samples=%d" 368 % (len(y), n_samples) 369 ) 371 if sample_weight is not None: 372 sample_weight = _check_sample_weight(sample_weight, X, DOUBLE)ValueError: Number of labels=179 does not match number of samples=241756

---------------------------------------------------------------------------ValueError Traceback (most recent call last)Cell In[9], line 5 2 clf=tree.DecisionTreeClassifier(max_depth=4) 4 # We fit the model using the training data----> 5 clf.fit(X_train, y_train) 8 clfFile ~/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs) 1144 estimator._validate_params() 1146 with config_context( 1147 skip_parameter_validation=( 1148 prefer_skip_nested_validation or global_skip_validation 1149 ) 1150 ):-> 1151 return fit_method(estimator, *args, **kwargs)File ~/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py:959, in DecisionTreeClassifier.fit(self, X, y, sample_weight, check_input) 928 @_fit_context(prefer_skip_nested_validation=True) 929 def fit(self, X, y, sample_weight=None, check_input=True): 930 """Build a decision tree classifier from the training set (X, y). 931 932 Parameters (...) 956 Fitted estimator. 957 """--> 959 super()._fit( 960 X, 961 y, 962 sample_weight=sample_weight, 963 check_input=check_input, 964 ) 965 return selfFile ~/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py:366, in BaseDecisionTree._fit(self, X, y, sample_weight, check_input, missing_values_in_feature_mask) 363 max_leaf_nodes = -1 if self.max_leaf_nodes is None else self.max_leaf_nodes 365 if len(y) != n_samples:--> 366 raise ValueError( 367 "Number of labels=%d does not match number of samples=%d" 368 % (len(y), n_samples) 369 ) 371 if sample_weight is not None: 372 sample_weight = _check_sample_weight(sample_weight, X, DOUBLE)ValueError: Number of labels=179 does not match number of samples=241756

You are fine-tuning a decision tree classifier for a marketing dataset. To prevent overfitting and ensure robust generalisability, you must adjust the depth of the decision tree after its initialisation but before it is fitted with data. Considering the decision tree `dt` has already been initialised with a random state, which of the following is the correct way to modify the tree's maximum depth?from sklearn.tree import DecisionTreeClassifierfrom sklearn.datasets import load_breast_cancerfrom sklearn.model_selection import train_test_split# Load datadata = load_breast_cancer()X = data.datay = data.target# Split dataX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# Initialise decision tree classifierdt = DecisionTreeClassifier(random_state=42)# [Your Code Heredt = DecisionTreeClassifier(max_depth=5, random_state=42)dt.set_params(max_depth=5)dt.set_params(max_depth=5).fit(X_train, y_train)dt.max_depth = 42

What method is used to fit a Decision Tree model in scikit-learn?Answer areafit()train()predict()apply()

Let's attempt to enhance our model's performance by setting the max_depth hyperparameter to 5.True or false? The decision tree model was improved by fitting it with a max_depth parameter of 5.FalseTrue