In decision trees, 'max_features' parameter is used to:Review LaterLimit the number of features considered for splitting at each nodeSet the maximum number of leaf nodesDefine the maximum depth of the treeControl the number of decision trees in a forest

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.

_______ is a technique that reduces the size of decision trees by removing branches of the trees to avoid overfitting in a fully grown decision tree.Answer choicesSelect only one optionREVISITCross-validationPruningTest-Train SplittingBootstrapping

# 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

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

Question 1These are all characteristics of decision trees, EXCEPT:1 pointThey have well rounded decision boundariesThey split nodes into leavesThey can be used for either classification or regressionThey segment data based on features to predict results