mlscorer 0.1

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ML Scorer

ML Scorer is the solution to your classification scores of ML algorithms.

Installation

pip install mlscorer

Preperation

Make a class mapping dictionary(map_class) using Method1 or Method2

Method 1

Make all the data categorical using following code snippet

    map_class = dict(zip(df.classes.astype("category").cat.codes, df.classes))
    print(map_class)

output: {1: 'positive', 0: 'negative'}

here, df is the Dataframe and classes is a column which may have class values like

• positive
• negative

[N.B. Don't change "category", it's a datatype]

or

Method 2

Make the Dictionary manually according to your classes

    map_class = {
	    1: 'positive',
	    0: 'negative'
    }

Usage

    from sklearn.linear_model import LogisticRegression
    import mlscorer as ms
    classifier = LogisticRegression()
    classifier.fit(X_train, y_train)
    y_pred = classifier.predict(X_test)
    ms.get_score_table(y_test, y_pred, map_class)

Output:

Parameters

y_test : target values of test set y_pred : predicted target values map_class : dict : your categoricl class mapping metrics : list : use one or more evaluation metric from f1, precision, recall or accuracy eg:

    ms.get_score_table(y_test, y_pred, map_class, metrics=['precision', 'recall'])