# Solved – How to interpret LIME results

LIME is a recent method that claims to help explaining individual predictions from classifiers agnostically. See e.g. arxiv or its implementation on github for details.

I am trying to understand what exactly it outputs. For that, I am using a trivial example: logistic regression.

Consider the following set of events:

``data  = [] for t in range(100000):     a = 1 - 2*numpy.random.random()  # U(-1, 1)     b = 1 - 2*numpy.random.random()  # U(-1, 1)     noise = numpy.random.logistic()     c = int(a + b + noise > 0)  # the target     data.append([a, b, c]) data = numpy.array(data)  x = data[:, :-1] y = data[:, -1] ``

This is a latent logistic process with parameters \$a_0 = 0\$, \$a_1 = a_2 = 1\$, of which logistic regression assymptotically fits.

Let us fit the data using logistic regression:

``classifier = sklearn.linear_model.LogisticRegression(C=1e10)  # C=inf => no regularization     classifier.fit(x, y) print(classifier.coef_)  # [[ 0.99092809  1.00551462]] ``

Now, lets apply LIME to it:

``explainer = lime.lime_tabular.LimeTabularExplainer(x, feature_names=['a', 'b'])  instance = numpy.array([1, 1]) explanation = explainer.explain_instance(instance, classifier.predict_proba, num_samples=100000) print(explanation.as_list()) ``

The result I get is something like this:

``[  ('a > 0.50', 0.2216),   ('b > 0.50', 0.2170) ] ``

the question is: what is this supposed to mean?

Contents

In the code implementaion repo you linked there's an example code which claims that explains, see cell 8

Note that LIME has discretized the features in the explanation. This is because we let discretize_continuous=True in the constructor (this is the default).

Lime Tabular Data Tutorial

If you modify the constructor to

``explainer = lime.lime_tabular.LimeTabularExplainer(x, feature_names=['a', 'b'],discretize_continuous=False) ``

then the output is something like

``[('b', 0.11223168027269199), ('a', 0.11110292313683988)] ``

I understood that both instances – discretized or not – are supposed to approximate a linear model in the vicinity of the feature example [1,1].

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