What does the following line of code achieve? centers = scaler.inverse_transform(km.cluster_centers_)where km = KMeans(n_clusters=K, random_state=42)scaler = StandardScaler()It predicts the cluster centres for the given dataIt reduces the dimensionality of the cluster centresIt scales the cluster centres to have a mean of 0 and a standard deviation of 1It transforms the cluster centres back to the original feature space

Consider the following code snippet: X = [[1, 2], [2, 3], [3, 4], [5, 6], [7, 8]] scaler = StandardScaler()X_scaled = scaler.fit_transform(X)model = AgglomerativeClustering(n_clusters=2, linkage='average')model.fit(X_scaled) Why do we use the fit_transform() method to scale the data?To increase the size of the datasetTo reduce the number of features in the datasetTo assign cluster labels to each data pointTo ensure each feature contributes equally to the distance calculations

What is the primary purpose of using the 'standard scaler' in preprocessing demographic data for clustering?To reduce the dimensionality of the dataTo convert categorical data into numerical dataTo normalise numerical data to have a mean of zero and a standard deviation of oneTo remove outliers from the dataset

ValueError Traceback (most recent call last)Cell In[176], line 6 4 # Standardize features 5 scaler = StandardScaler()----> 6 X_train = scaler.fit_transform(X_train) 7 X_test = scaler.transform(X_test) 9 # Train Random Forest RegressorFile ~\anaconda3\lib\site-packages\sklearn\utils\_set_output.py:313, in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs) 311 @wraps(f) 312 def wrapped(self, X, *args, **kwargs):--> 313 data_to_wrap = f(self, X, *args, **kwargs) 314 if isinstance(data_to_wrap, tuple): 315 # only wrap the first output for cross decomposition 316 return_tuple = ( 317 _wrap_data_with_container(method, data_to_wrap[0], X, self), 318 *data_to_wrap[1:], 319 )File ~\anaconda3\lib\site-packages\sklearn\base.py:1098, in TransformerMixin.fit_transform(self, X, y, **fit_params) 1083 warnings.warn( 1084 ( 1085 f"This object ({self.__class__.__name__}) has a `transform`" (...) 1093 UserWarning, 1094 ) 1096 if y is None: 1097 # fit method of arity 1 (unsupervised transformation)-> 1098 return self.fit(X, **fit_params).transform(X) 1099 else: 1100 # fit method of arity 2 (supervised transformation) 1101 return self.fit(X, y, **fit_params).transform(X)File ~\anaconda3\lib\site-packages\sklearn\preprocessing\_data.py:878, in StandardScaler.fit(self, X, y, sample_weight) 876 # Reset internal state before fitting 877 self._reset()--> 878 return self.partial_fit(X, y, sample_weight)File ~\anaconda3\lib\site-packages\sklearn\base.py:1473, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs) 1466 estimator._validate_params() 1468 with config_context( 1469 skip_parameter_validation=( 1470 prefer_skip_nested_validation or global_skip_validation 1471 ) 1472 ):-> 1473 return fit_method(estimator, *args, **kwargs)File ~\anaconda3\lib\site-packages\sklearn\preprocessing\_data.py:914, in StandardScaler.partial_fit(self, X, y, sample_weight) 882 """Online computation of mean and std on X for later scaling. 883 884 All of X is processed as a single batch. This is intended for cases (...)

Which data scaling technique centers the data around the mean and scales it to have a standard deviation of 1?Review LaterMin-Max ScalingZ-Score StandardizationRobust ScalingLog Transformation

The transformation that is used to alter the size of an object isa.Scalingb.Translationc.Rotationd.Reflection