done

I learned about L1 and L2 Normalization, and log transformation and revised the concepts of Standardization ( Standard Scalar, Min-Max Scalar, Max Absolute Scalar, Robust Scalar)

Done

i understand thank jazakumulah kharn

from sklearn.preprocessing import Normalizer data = [[1, 1, 1], [1, 1, 0], [1, 0, 0]] normalizer = Normalizer(norm='12') print(normalizer.fit_transform(data)) showing error plz help meFile c:UsersdeLLminiconda3envspython_mlLibsite-packagessklearnutils_set_output.py:157, in _wrap_method_output..wrapped(self, X, *args, **kwargs) 155 @wraps(f) 156 def wrapped(self, X, *args, **kwargs): --> 157 data_to_wrap = f(self, X, *args, **kwargs) 158 if isinstance(data_to_wrap, tuple): 159 # only wrap the first output for cross decomposition 160 return_tuple = ( 161 _wrap_data_with_container(method, data_to_wrap[0], X, self), 162 *data_to_wrap[1:], 163 )File c:UsersdeLLminiconda3envspython_mlLibsite-packagessklearnbase.py:916, in TransformerMixin.fit_transform(self, X, y, **fit_params) 912 # non-optimized default implementation; override when a better 913 # method is possible for a given clustering algorithm 914 if y is None: 915 # fit method of arity 1 (unsupervised transformation) --> 916 return self.fit(X, **fit_params).transform(X) 917 else:

I have done this lecture with 100% practice.

I have learned L1 and L2 with 100% practice.

L1 is used for the sum of the absolute values is 1 in each row. L2 is used for text data normalization.