14. A Junior analyst trained an Sklearn random forest classifier to predict the winner of football matches. The model is performing well on the train data set. When the model was evaluated on the test dataset it performed poorly. Which of the following will be useful in solving this problem?. Select all correct.increasing the min samples leaf in each tree of the forestreducing the number of trees in the forestdecreasing the max depth of each tree in the forestreducing the min samples split in each tree of the forest

Which of the following is a technique used to reduce overfitting in the Random Forest algorithm?Review LaterDecreasing the number of estimatorsIncreasing the maximum depth of the decision treesIncreasing the subsample sizeIncreasing the learning rate

Which of the following is a hyperparameter of the Random Forest algorithm?Review LaterLearning rateNumber of estimatorsMaximum depthSubsample size

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

# 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

Which of the following machine learning algorithm is based upon the idea of bagging?Random-forestRegressionClassificationDecision treeSAVE