Automate machine learning classification task report for Pak Zuherman
Project description
Classification Reportzr
Automate machine learning classification task report for Pak Zuherman
Install
pip install -U classification-reportzr
Test
pytest -v
Usage
Setting-up the experiment
from sklearn import datasets
from sklearn.svm import SVC
from reporterzr import Reporterzr
iris = datasets.load_iris()
samples, labels = iris.data[:-1], iris.target[:-1]
param_grid = {
'C': [10,50,100],
'gamma': [0.005,0.05,0.5],
'kernel': ['poly', 'rbf', 'linear']
}
svc_reporter = Reporterzr(SVC, param_grid)
Run The Experiment
# `test_sizes` defaults to [0.1, ..., 0.9]
# `repetition` defaults to 10
report = svc_reporter.run_experiment(samples, labels, test_sizes=[0.1, 0.2], repetition=3)
print(report)
prints
Test Size C gamma kernel Train Accuracies Max Train Mean Train \
0 0.1 10 0.005 poly [0.873, 0.896, 0.881] 0.896 0.883
1 0.1 10 0.005 rbf [0.978, 0.97, 0.978] 0.978 0.975
2 0.1 10 0.005 linear [0.985, 0.985, 0.978] 0.985 0.983
3 0.1 10 0.050 poly [0.985, 0.97, 0.978] 0.985 0.978
4 0.1 10 0.050 rbf [0.993, 0.993, 0.993] 0.993 0.993
Stdev Train Test Accuracies Max Test Mean Test Stdev Test \
0 0.010 [1.0, 0.8, 0.867] 1.000 0.889 0.083
1 0.004 [0.933, 0.933, 0.8] 0.933 0.889 0.063
2 0.003 [0.933, 1.0, 0.933] 1.000 0.955 0.032
3 0.006 [0.933, 0.933, 1.0] 1.000 0.955 0.032
4 0.000 [1.0, 0.933, 1.0] 1.000 0.978 0.032
Experiment Times
0 [0.008, 0.001, 0.001]
1 [0.002, 0.001, 0.002]
2 [0.001, 0.001, 0.001]
3 [0.001, 0.001, 0.001]
4 [0.001, 0.001, 0.001]
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