Metadata-Version: 2.1
Name: classification-reportzr
Version: 0.0.1b4
Summary: Automate machine learning classification task report for Pak Zuherman
Home-page: https://github.com/khalidm31415/classification-reportzr
License: UNKNOWN
Description: # Classification Reportzr
        
        Automate machine learning classification task report for Pak Zuherman
        
        ## Install
        
        ```bash
        pip install -U classification-reportzr
        ```
        
        ## Test
        
        ```bash
        pytest -v
        ```
        
        ## Usage
        
        ### Setting-up the experiment
        
        ```python
        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
        
        ```python
        # `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
        ```
        
Keywords: classification report,laporan klasifikasi,zuherman,zr
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
