Classification Model's parameters produce different results

Question

I’m working on SVC model for classification and I faced different accuracy result in each time I changed the values of the parameters (svc__gamma, svc__kernel and svc__C), I read the documentation of Sklearn but I could not understand what those parameters mean, I have Three questions :

What did those parameters indicate to?
How its effect Accuracy each time I change it?
What is the correct parameter values?

the result of accuracy is 0.70, but when I delete svc__gamma and svc__C , the result increases up to 0.76.

    pipe = make_pipeline(TfidfVectorizer(),
                             SVC())
param_grid = {'svc__kernel': ['rbf', 'linear', 'poly'],
                     'svc__gamma': [0.1, 1, 10, 100],
                     'svc__C': [0.1, 1, 10, 100]}

svc_model = GridSearchCV(pipe, param_grid, cv=3)
svc_model.fit(X_train, Y_train)

prediction = svc_model.predict(X_test)
print(f"Accuracy score is {accuracy_score(Y_test, prediction):.2f}")
print(classification_report(Y_test, prediction))

Asked By: Asma Alghamdi

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Source

Answer 1

to 1.

gamma is a parameter of the gaussian bell curve, so it should only
affect the RBF( Gaussian Kernel)
C is the paramter of the optimization problem, the inverse of the Lagrangian multiplier

to. 2.

get familiar with the mathematical background to fully understand how they affect your accuracy (sidenote: Accuracy is usuallly no reliable measure, but depends on context)

to 3.

there are no ‘correct’ parameters. They depend on the context, data and the goal you want to achive. Usually there is a tradeoff between how good the algorithm works on test data and how it works on new data ( overfitting vs. underfitting)

I hope that helps as a first step 🙂
for further information I suggest SVM.

Answered By: Epimetheus

Classification Model's parameters produce different results

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