Metadata-Version: 2.1
Name: classification-reportzr
Version: 0.0.1b6
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],
            'kernel': ['poly', 'rbf', 'kernel']
        }
        svc_reporter = Reporterzr(SVC, param_grid)
        ```
        
        ### Run The Experiment
        
        ```python
        # `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       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
        ```
        
Keywords: classification report,laporan klasifikasi,zuherman,zr
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
