mldissect - model agnostic explanations
Project description
mldissect
mldissect is model agnostic predictions explainer, library can show contribution of each feature of your prediction value.
Features
Supports predictions explanations for classification and regression
Easy to use API.
Works with pandas and numpy
Installation
Installation process is simple, just:
$ pip install mldissect
Basic Usage
# lets train a model
boston = load_boston()
columns = list(boston.feature_names)
X, y = boston['data'], boston['target']
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=.2, random_state=seed
)
clf = LassoCV()
clf.fit(X_train, y_train)
# select first observation in test split
observation = X_test[0]
# RegressionExplainer uses training data or sample of training data
# for large dataset to figure out contributions of each feature
explainer = RegressionExplainer(clf, X_train, columns)
result = explainer.explain(observation)
# print/visualize explanation
explanation = Explanation(result)
explanation.print()
result:
+----------+---------+--------------------+ | Feature | Value | Contribution | +----------+---------+--------------------+ | baseline | - | 22.611881188118804 | | LSTAT | 7.34 | 3.6872 | | PTRATIO | 16.9 | 1.3652 | | CRIM | 0.06724 | 0.2323 | | B | 375.21 | 0.1195 | | RM | 6.333 | 0.0411 | | INDUS | 3.24 | 0.0312 | | CHAS | 0.0 | 0.0 | | NOX | 0.46 | 0.0 | | TAX | 430.0 | -0.3794 | | AGE | 17.2 | -0.5127 | | ZN | 0.0 | -0.6143 | | DIS | 5.2146 | -1.0792 | | RAD | 4.0 | -1.0993 | +----------+---------+--------------------+
Algorithm
Algorithm is based on ideas describe in paper “Explanations of model predictions with live and breakDown packages” https://arxiv.org/abs/1804.01955
Difference with pyBreakDown
pyBreakDown is similar project, but there is key differences:
mldissect is maintained
Has tests and good code coverage.
Classification is working properly.
Multi class support.
Top down approach is not implemented.
Friendly license.
Requirements
CHANGES
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