Given that we want to evaluate the performance of 'n' different machine learning models on the same data, why would the following splitting mechanism be incorrect: def get_splits(): df rnd pd.DataFrame(...) np.random. rand (len (df)) train df[ rnd <0.8] valid df[ rnd >= 0.8 & rnd < 0.9 ] test =df[ rnd >= 0.9] return train, valid, test #Model 1 from sklearn.tree import Decision Tree Classifier train, valid, test = get_splits() ... #Model 2. from sklearn.linear_model import Logistic Regression train, valid, test = get_splits ()
Given that we want to evaluate the performance of 'n' different machine learning models on the same data, why would the following splitting mechanism be incorrect: def get_splits(): df rnd pd.DataFrame(...) np.random. rand (len (df)) train df[ rnd <0.8] valid df[ rnd >= 0.8 & rnd < 0.9 ] test =df[ rnd >= 0.9] return train, valid, test #Model 1 from sklearn.tree import Decision Tree Classifier train, valid, test = get_splits() ... #Model 2. from sklearn.linear_model import Logistic Regression train, valid, test = get_splits ()
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![Given that we want to evaluate the performance of 'n' different machine learning models on the same data,
why would the following splitting mechanism be incorrect:
def get_splits():
df
pd.DataFrame(...)
rnd = np.random.rand(len(df))
train =df[ rnd < 0.8 ]
valid =df[ rnd >= 0.8 & rnd < 0.9 ]
test =df[ rnd >= 0.9 ]
return train, valid, test
#Model 1
from sklearn.tree import Decision Tree Classifier
train, valid, test
=
get_splits()
#Model 2
from sklearn.linear_model import Logistic Regression
train, valid, test get_splits()
=](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F96797c74-164a-4bd8-b93c-afd86a760e73%2Ff27cc513-3207-4fdf-a5d3-3eef9d17a8cd%2Fblfyd9q_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Given that we want to evaluate the performance of 'n' different machine learning models on the same data,
why would the following splitting mechanism be incorrect:
def get_splits():
df
pd.DataFrame(...)
rnd = np.random.rand(len(df))
train =df[ rnd < 0.8 ]
valid =df[ rnd >= 0.8 & rnd < 0.9 ]
test =df[ rnd >= 0.9 ]
return train, valid, test
#Model 1
from sklearn.tree import Decision Tree Classifier
train, valid, test
=
get_splits()
#Model 2
from sklearn.linear_model import Logistic Regression
train, valid, test get_splits()
=
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