Metadata-Version: 2.1
Name: plotclassification
Version: 0.0.4
Summary: This package perform different way to visualize machine learning  and deep learning classification results
Home-page: https://github.com/vishalbpatil1/ml_classification_plot
Author: Vishal Patil
Author-email: vishalbpatil1@gmail.com
License: MIT
Description: Plots of Classifier Performance :-
        When it comes to machine learning and deep learning task there are many ways to plot the performance of a classifier.
        There is  metrics to compare different estimators like accuracy, precision, recall and f1 score.All of the common classification metrics are calculated from true positive, true negative, false positive and false negative incidents. The most popular plots are definitely ROC   curve, PRC and the confusion matrix.
        
          
        
        
        
        ### There are four ways to check if the predictions are right or wrong:
        ```bash
          1. TN / True Negative: the case was negative and predicted negative
          2. TP / True Positive: the case was positive and predicted positive
          3. FN / False Negative: the case was positive but predicted negative
          4. FP / False Positive: the case was negative but predicted positive
        
          Precision:- Accuracy of positive predictions  = TP/(TP + FP)
          Recall:- Fraction of positives that were correctly identified = TP/(TP+FN)
          F1 Score = 2*(Recall * Precision) / (Recall + Precision)
        
        ```
        
        ## User installation :
        If you already have a working installation of numpy and pandas, the easiest way to install plotly_ml_classification is using pip
        ```bash
        pip install plotclassification
        ```
        
        ## This package depend on other packages:
        ```bash
          1.numpy
          2.pandas
          3.sklearn 
          4.plotly
        ```
        
        ## Usage
        
        ```python
        
        # import libraries
        import plotclassification 
        from sklearn import datasets 
        from sklearn.linear_model import LogisticRegression
        from sklearn.model_selection import train_test_split 
        
        
        # Load data
        iris = datasets.load_iris()
        # Create feature matrix
        features = iris.data
        # Create target vector 
        target = iris.target
        
        #create list of classname 
        class_names = iris.target_names
        class_names
        
        
        # Create training and test set 
        x_train, x_test, y_train, y_test = train_test_split(features,
                                                             target,
                                                             test_size=0.9, 
                                                             random_state=1)
        
        
        # Create logistic regression 
        classifier = LogisticRegression()
        
        # Train model and make predictions
        model = classifier.fit(x_train, y_train)
        
        # create predicted probabilty matrix 
        y_test__scores = model.predict_proba(x_test)
        
        # initialize parameters value
        plot=plotclassification.plot(y=y_test,
        	         y_predict_proba=y_test__scores,
        	         class_name=['Class 1','class 2','class 3'])
        
        ```
        
        ```python
        plot.class_name
        ['Class 1', 'class 2', 'class 3']
        
        # classification report plot
        plot.plot_classification_report()
        
        #  confusion matrix plot
        plot.plot_confusion_matrix()
        
        # precision recall curve plot
        plot.plot_precision_recall_curve()
        
        # roc plot
        plot.plot_roc()
        
        # predicted probability histogram plot
        plot_probability_histogram()
        
        ```
        [Github file source](https://github.com/vishalbpatil1/ml_classification_plot)
        
        
        Change Log
        ==========
        0.0.1 (06/03/2021)
        -------------------
        - First Release
        -------------------
        
        
Keywords: plot classification
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
