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How to perform (modified) t-test for multiple variables and multiple models on Python (Machine Learning)

0

0

$begingroup$

I have created and analyzed around 16 machine learning models using WEKA. Right now, I have a CSV file which shows the models' metrics (such as percent_correct, F-measure, recall, precision, etc.). I am trying to conduct a (modified) student's t-test on these models. I am able to conduct one (according to THIS link) where I compare only ONE variable common to only TWO models. I want to perform a (or multiple) t-tests with MULTIPLE variables and MULTIPLE models at once.

As mentioned, I can only perform the test with one variable (let's say F-measure) among two models (let's say decision table and neural net).

Here's the code for that. I am performing a Kolmogorov-Smirnov test (modified t):

from matplotlib import pyplot
from pandas import read_csv, DataFrame
from scipy.stats import ks_2samp

results = DataFrame()
results['A'] = read_csv('LMT (f-measure).csv', header=None).values[:, 0]
results['B'] = read_csv('LWL (f-measure).csv', header=None).values[:, 0]
print(results.describe())
results.boxplot()
pyplot.show()
results.hist()
pyplot.show()

value, pvalue = ks_2samp(results['A'], results['B'])
alpha = 0.05
print(value, pvalue)
if pvalue > alpha:
 print('Samples are likely drawn from the same distributions (fail to reject H0)')
else:
 print('Samples are likely drawn from different distributions (reject H0)')

Any ideas?

machine-learning python pandas statistics scipy

share|improve this question

asked 33 mins ago

Shounak RayShounak Ray

1

$endgroup$

add a comment | 

0

0

$begingroup$

I have created and analyzed around 16 machine learning models using WEKA. Right now, I have a CSV file which shows the models' metrics (such as percent_correct, F-measure, recall, precision, etc.). I am trying to conduct a (modified) student's t-test on these models. I am able to conduct one (according to THIS link) where I compare only ONE variable common to only TWO models. I want to perform a (or multiple) t-tests with MULTIPLE variables and MULTIPLE models at once.

As mentioned, I can only perform the test with one variable (let's say F-measure) among two models (let's say decision table and neural net).

Here's the code for that. I am performing a Kolmogorov-Smirnov test (modified t):

from matplotlib import pyplot
from pandas import read_csv, DataFrame
from scipy.stats import ks_2samp

results = DataFrame()
results['A'] = read_csv('LMT (f-measure).csv', header=None).values[:, 0]
results['B'] = read_csv('LWL (f-measure).csv', header=None).values[:, 0]
print(results.describe())
results.boxplot()
pyplot.show()
results.hist()
pyplot.show()

value, pvalue = ks_2samp(results['A'], results['B'])
alpha = 0.05
print(value, pvalue)
if pvalue > alpha:
 print('Samples are likely drawn from the same distributions (fail to reject H0)')
else:
 print('Samples are likely drawn from different distributions (reject H0)')

Any ideas?

machine-learning python pandas statistics scipy

share|improve this question

asked 33 mins ago

Shounak RayShounak Ray

1

$endgroup$

add a comment | 

$begingroup$

I have created and analyzed around 16 machine learning models using WEKA. Right now, I have a CSV file which shows the models' metrics (such as percent_correct, F-measure, recall, precision, etc.). I am trying to conduct a (modified) student's t-test on these models. I am able to conduct one (according to THIS link) where I compare only ONE variable common to only TWO models. I want to perform a (or multiple) t-tests with MULTIPLE variables and MULTIPLE models at once.

As mentioned, I can only perform the test with one variable (let's say F-measure) among two models (let's say decision table and neural net).

Here's the code for that. I am performing a Kolmogorov-Smirnov test (modified t):

from matplotlib import pyplot
from pandas import read_csv, DataFrame
from scipy.stats import ks_2samp

results = DataFrame()
results['A'] = read_csv('LMT (f-measure).csv', header=None).values[:, 0]
results['B'] = read_csv('LWL (f-measure).csv', header=None).values[:, 0]
print(results.describe())
results.boxplot()
pyplot.show()
results.hist()
pyplot.show()

value, pvalue = ks_2samp(results['A'], results['B'])
alpha = 0.05
print(value, pvalue)
if pvalue > alpha:
 print('Samples are likely drawn from the same distributions (fail to reject H0)')
else:
 print('Samples are likely drawn from different distributions (reject H0)')

Any ideas?

machine-learning python pandas statistics scipy

share|improve this question

asked 33 mins ago

Shounak RayShounak Ray

1

$endgroup$

from matplotlib import pyplot
from pandas import read_csv, DataFrame
from scipy.stats import ks_2samp

results = DataFrame()
results['A'] = read_csv('LMT (f-measure).csv', header=None).values[:, 0]
results['B'] = read_csv('LWL (f-measure).csv', header=None).values[:, 0]
print(results.describe())
results.boxplot()
pyplot.show()
results.hist()
pyplot.show()

value, pvalue = ks_2samp(results['A'], results['B'])
alpha = 0.05
print(value, pvalue)
if pvalue > alpha:
 print('Samples are likely drawn from the same distributions (fail to reject H0)')
else:
 print('Samples are likely drawn from different distributions (reject H0)')

share|improve this question

asked 33 mins ago

Shounak RayShounak Ray

1

share|improve this question

asked 33 mins ago

Shounak RayShounak Ray

1

asked 33 mins ago

Shounak RayShounak Ray

1

asked 33 mins ago

Shounak RayShounak Ray

1