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, 0.948, 0.963] 0.970 0.960 0.009 [0.933, 1.0, 1.0] 1.000 0.978 0.032
1 0.1 10 0.050 [0.993, 0.985, 0.993] 0.993 0.990 0.004 [1.0, 1.0, 0.933] 1.000 0.978 0.032
2 0.1 10 0.500 [0.978, 0.978, 0.978] 0.978 0.978 0.000 [1.0, 1.0, 1.0] 1.000 1.000 0.000
3 0.1 50 0.005 [0.993, 0.993, 0.978] 0.993 0.988 0.007 [1.0, 1.0, 1.0] 1.000 1.000 0.000
4 0.1 50 0.050 [0.97, 0.978, 0.993] 0.993 0.980 0.010 [1.0, 1.0, 0.933] 1.000 0.978 0.032
5 0.1 50 0.500 [0.978, 0.978, 0.993] 0.993 0.983 0.007 [1.0, 1.0, 0.933] 1.000 0.978 0.032
6 0.1 100 0.005 [0.993, 0.985, 0.993] 0.993 0.990 0.004 [1.0, 1.0, 1.0] 1.000 1.000 0.000
7 0.1 100 0.050 [0.97, 0.985, 0.993] 0.993 0.983 0.010 [1.0, 0.867, 0.933] 1.000 0.933 0.054
8 0.1 100 0.500 [1.0, 0.993, 0.985] 1.000 0.993 0.006 [0.8, 0.933, 1.0] 1.000 0.911 0.083
9 0.2 10 0.005 [0.975, 0.958, 0.975] 0.975 0.969 0.008 [0.9, 0.933, 0.967] 0.967 0.933 0.027
10 0.2 10 0.050 [0.992, 0.983, 0.992] 0.992 0.989 0.004 [1.0, 1.0, 0.967] 1.000 0.989 0.016
11 0.2 10 0.500 [0.983, 0.983, 0.983] 0.983 0.983 0.000 [0.9, 0.967, 0.933] 0.967 0.933 0.027
12 0.2 50 0.005 [0.992, 0.992, 0.992] 0.992 0.992 0.000 [0.967, 0.967, 1.0] 1.000 0.978 0.016
13 0.2 50 0.050 [1.0, 0.992, 0.975] 1.000 0.989 0.010 [0.933, 0.933, 1.0] 1.000 0.955 0.032
14 0.2 50 0.500 [0.983, 0.983, 0.975] 0.983 0.980 0.004 [0.867, 0.933, 0.967] 0.967 0.922 0.042
15 0.2 100 0.005 [0.983, 0.983, 0.992] 0.992 0.986 0.004 [1.0, 1.0, 1.0] 1.000 1.000 0.000
16 0.2 100 0.050 [0.966, 0.975, 0.983] 0.983 0.975 0.007 [0.967, 0.967, 0.967] 0.967 0.967 0.000
17 0.2 100 0.500 [0.992, 0.992, 0.983] 0.992 0.989 0.004 [0.933, 0.933, 0.967] 0.967 0.944 0.016
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