CODEWITHSUNDEEP
pythonmachine-learningscikit-learnlogistic-regression
minks
minks

Reputation: 3039

How can the output of a model be displayed?

I am performing a machine learning task wherein I am using logistic regression for topic classification.

If this is my code:

model= LogisticRegression()
model= model.fit(mat_tmp, label_tmp)

y_train_pred = model.predict(mat_tmp_test)

print(metrics.accuracy_score(label_tmp_test, y_train_pred))

Is there a way I can output what exactly is happening inside the model. Like probably a working example of what my model is doing? Like maybe displaying 2-3 documents and how they are being classified?

Upvotes: 0

Views: 190

Answers (1)

David Frank
David Frank

Reputation: 6092

In order to be fully aware of what is happening in your model, you must first take some time to study the logistic regression algorithm (eg. from lecture notes or Wikipedia). As with other supervised techniques, logistic regression has hyper-parameters and parameters. Hyper-parameters basically specify how your algorithm runs, which you must provide at initialisation (ie. before it sees any data). For example, you could have prior information about the distribution of classes, which then would be a hyper-parameter. Parameters are "learnt" from your data.

Once you understand the algorithm, the interesting question will be what the parameters of your model are (recall that these are retrieved from the data). By visiting the documentation, you find in the attributes section, that this classifier has 3 parameters, which you can access by their field names.

If you are not interested in such details, but only want to assess the accuracy of your classifier, a useful technique is cross-validation. You split your labeled data into k equal sized subsets, and train your classifier using k-1 of them. Then you evaluate the trained classifier on the remaining 1 subset and calculate the accuracy (ie. what proportion of the data could be predicted properly). This method has its drawbacks, but proves to be very useful in general.

Upvotes: 2

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