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]
}
svc_reporter = Reporterzr(SVC, param_grid)
Run The Experiment
# `test_sizes` defaults to [0.1, ..., 0.9]
# `repetition` defaults to 10
svc_reporter.run_experiment(samples, labels, test_sizes=[0.1, 0.2], repetition=3)
prints
Test Size C gamma Train Accuracies Max Train Mean Train Stdev Train Test Accuracies Max Test Mean Test Stdev Test
0 0.1 10 0.005 [0.97, 0.97, 0.97] 0.970 0.970 0.0 [0.933, 0.933, 0.933] 0.933 0.933 0.0
1 0.1 10 0.050 [0.993, 0.993, 0.993] 0.993 0.993 0.0 [0.867, 0.867, 0.867] 0.867 0.867 0.0
2 0.1 10 0.500 [0.985, 0.985, 0.985] 0.985 0.985 0.0 [0.867, 0.867, 0.867] 0.867 0.867 0.0
3 0.1 50 0.005 [0.993, 0.993, 0.993] 0.993 0.993 0.0 [0.933, 0.933, 0.933] 0.933 0.933 0.0
4 0.1 50 0.050 [0.985, 0.985, 0.985] 0.985 0.985 0.0 [0.867, 0.867, 0.867] 0.867 0.867 0.0
5 0.1 50 0.500 [0.993, 0.993, 0.993] 0.993 0.993 0.0 [0.867, 0.867, 0.867] 0.867 0.867 0.0
6 0.1 100 0.005 [0.993, 0.993, 0.993] 0.993 0.993 0.0 [0.867, 0.867, 0.867] 0.867 0.867 0.0
7 0.1 100 0.050 [0.985, 0.985, 0.985] 0.985 0.985 0.0 [0.867, 0.867, 0.867] 0.867 0.867 0.0
8 0.1 100 0.500 [0.985, 0.985, 0.985] 0.985 0.985 0.0 [0.867, 0.867, 0.867] 0.867 0.867 0.0
9 0.2 10 0.005 [0.958, 0.958, 0.958] 0.958 0.958 0.0 [1.0, 1.0, 1.0] 1.000 1.000 0.0
10 0.2 10 0.050 [0.992, 0.992, 0.992] 0.992 0.992 0.0 [1.0, 1.0, 1.0] 1.000 1.000 0.0
11 0.2 10 0.500 [0.983, 0.983, 0.983] 0.983 0.983 0.0 [1.0, 1.0, 1.0] 1.000 1.000 0.0
12 0.2 50 0.005 [0.983, 0.983, 0.983] 0.983 0.983 0.0 [1.0, 1.0, 1.0] 1.000 1.000 0.0
13 0.2 50 0.050 [0.966, 0.966, 0.966] 0.966 0.966 0.0 [0.967, 0.967, 0.967] 0.967 0.967 0.0
14 0.2 50 0.500 [0.975, 0.975, 0.975] 0.975 0.975 0.0 [0.967, 0.967, 0.967] 0.967 0.967 0.0
15 0.2 100 0.005 [0.992, 0.992, 0.992] 0.992 0.992 0.0 [1.0, 1.0, 1.0] 1.000 1.000 0.0
16 0.2 100 0.050 [0.975, 0.975, 0.975] 0.975 0.975 0.0 [1.0, 1.0, 1.0] 1.000 1.000 0.0
17 0.2 100 0.500 [0.992, 0.992, 0.992] 0.992 0.992 0.0 [0.967, 0.967, 0.967] 0.967 0.967 0.0
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