---------------------------------------------------------------------------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

# 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

---------------------------------------------------------------------------InvalidParameterError Traceback (most recent call last)Cell In[13], line 2 1 plt.figure(figsize=(40,30))----> 2 tree.plot_tree(clf, fontsize=14, feature_names=X.columns, filled=True, proportion=True) 3 plt.savefig('decision_tree.png', bbox_inches="tight")File ~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:201, in validate_params.<locals>.decorator.<locals>.wrapper(*args, **kwargs) 198 to_ignore += ["self", "cls"] 199 params = {k: v for k, v in params.arguments.items() if k not in to_ignore}--> 201 validate_parameter_constraints( 202 parameter_constraints, params, caller_name=func.__qualname__ 203 ) 205 try: 206 with config_context( 207 skip_parameter_validation=( 208 prefer_skip_nested_validation or global_skip_validation 209 ) 210 ):File ~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:95, in validate_parameter_constraints(parameter_constraints, params, caller_name) 89 else: 90 constraints_str = ( 91 f"{', '.join([str(c) for c in constraints[:-1]])} or" 92 f" {constraints[-1]}" 93 )---> 95 raise InvalidParameterError( 96 f"The {param_name!r} parameter of {caller_name} must be" 97 f" {constraints_str}. Got {param_val!r} instead." 98 )InvalidParameterError: The 'feature_names' parameter of plot_tree must be an instance of 'list' or None. Got Index(['GP', 'TVOL', 'MOM12', 'EPQ', 'INFL', '10YTR', 'UNRATE', 'UMCSENT'], dtype='object') instead.<Figure size 4000x3000 with 0 Axes>

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

Consider the following decision tree.What is the value associated with node 3?

---------------------------------------------------------------------------NameError Traceback (most recent call last)Cell In[12], line 1----> 1 plt.figure(figsize (40,30)) 2 tree.plot_tree(clf,fontsize=14,feature_name=X.columns,filled=True,proportion=True) 3 plt.savefig('decision_tree.png',bbox_inches="tight")NameError: name 'figsize' is not defined