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

What is the likely effect of increasing the max_depth parameter in a Decision Tree model?Higher max_depth values reduce the likelihood of capturing complex relationships in the data.Lower max_depth values may lead to increased model complexity and a higher risk of underfittingHigher max_depth values may lead to decreased model complexity and a lower risk of overfitting.Higher max_depth values may lead to increased model complexity and a higher risk of overfitting.

# 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

Which of the following is true about “max_depth” parameter in Gradient Boosting?Answer choicesSelect an optionIncreasing value of max_depth overfits the data and Higher value is betterIncreasing the value of max_depth overfits the data and lower value is betterDecreasing value of max_depth overfits the data and lower value is betterDecreasing value of max_depth underfits the data and lower value is better