Hi Shalu
decision_function returns (signed) distance to each of separating
hyperplanes. There's one hyperplane for each pair of classes, so in case of
2 classes there'd be one hyperplane. Iris dataset contains 3 classes, so
there are 3 possible pairs, and thus 3 columns in the result of
decision_function.
On Thu, Feb 26, 2015 at 7:28 PM, shalu jhanwar <shalu.jhanwa...@gmail.com>
wrote:
> Hi guys,
>
> I'm facing a problem when I am using decision_function in svm classifier.
> I have *2 classes*, but decision function is returning an array with *one
> column* only. Decision_function is working fine with iris dataset. I'm
> afraid if I am doing something wrong while reading my files/data.
>
> Any suggestions where might be the problem?
>
> thanks!
>
> P.S.: Please find here a chunk of the code:
>
> I'm reading my tab-delimited files as:
>
> data_cols = [2, 3, 4, 5, 6]
>
> X_train = np.loadtxt(data_file, usecols=data_cols, delimiter = "\t")
>
> y_train = np.genfromtxt(data_file, usecols = label_col, delimiter = "\t")
>
> X_test = np.loadtxt(test_file, usecols=data_cols, delimiter = "\t")
>
> y_test = np.genfromtxt(test_file, usecols = label_col, delimiter = "\t")
> *Although it converts all the values in scientific format while reading
> the files*
> And I am calling decision functions as:
>
> rbf_svc = svm.SVC(kernel='rbf',probability=True, C=C_val, gamma = g_val)
>
> scores = rbf_svc.fit(X_train, y_train).score(X_test, y_test)
>
> y_pred = rbf_svc.predict(X_test)
>
> #y_score = rbf_svc.predict_proba(X_test)
>
> y_score = rbf_svc.decision_function(X_test)
>
> predict_proba gave array with 2 columns but decision_function returned an
> array with one column only.
>
>
>
>
>
>
>
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