Poor probability results for SVM text classification - python-3.x

Question

I'm fairly new to machine learning technologies and I'm using sklearn and SVC to perform date classification on texts as apart of a project, but I'm getting incredibly low probabiltiy scores.
I have a corpus of 13 texts all authored at different dates ranging from 598 to 1358 (which I use as classes) stored in a train and test file directory, I use a CountVectoriser and TfidfTransformer to prepare the data and pickle my results for later use:
src = "../data/datasets/pickledWordLists/"
corpus = []
for filename in os.listdir(src):
with (open(os.path.join(src, filename), "rb")) as openfile:
while True:
try:
text = pickle.load(openfile)
text = ' '.join(word for word in text)
corpus.append(text)
except EOFError:
break
src = "../data/datasets/TestsPickled/"
for filename in os.listdir(src):
with (open(os.path.join(src, filename), "rb")) as openfile:
while True:
try:
text = pickle.load(openfile)
text = ' '.join(word for word in text)
corpus.append(text)
except EOFError:
break
vectorizer = CountVectorizer()
vectorizer.fit(corpus)
vector = vectorizer.transform(corpus)
tfidf_transformer = TfidfTransformer()
vector = tfidf_transformer.fit_transform(vector)
with open('../data/datasets/labeledData/training/train_matrices/corpus.pickle', 'wb') as handle:
pickle.dump(vector, handle)
After this I load it back in:
train_sparse = []
with open('../data/datasets/labeledData/training/train_matrices/corpus.pickle', 'rb') as handle:
train_sparse = pickle.load(handle)
train_set = np.array(train_sparse.toarray()[0:9])
test_set = np.array(train_sparse.toarray()[10:14])
labels = [1028, 1107, 1358, 598, 707, 875, 884, 890, 988]
I then fit a SVC with the training data and class labels:
clf = SVC(kernel='linear', C=100, cache_size=300, class_weight='balanced', coef0=10.0,
decision_function_shape='ovo', degree=10, gamma='auto',
max_iter=-1, probability=True, random_state=None, shrinking=False,
tol=1, verbose=False)
clf.fit(train_set, labels)
Experimenting with the fitted SVC, and to get some confidence I test it on one of the examples it was actually trained on (clf.predict([train_sparse.toarray()[6]], class 884) expecting a close to 1.0 probabiltiy score for that class. I actually get a very poor result with an incorrect classification.
Actual Class: 884
Predicted Class: [988]
Class: 1028 probability: 0.13680521863292022 %
Class: 1107 probability: 0.1372151835630488 %
Class: 1358 probability: 0.09314753496099398 %
Class: 598 probability: 0.11216304253012621 %
Class: 707 probability: 0.07705449472997644 %
Class: 875 probability: 0.07702437742491991 %
Class: 884 probability: 0.11694844959109225 %
Class: 890 probability: 0.12739816653603753 %
Class: 988 probability: 0.1222435320308847 %
Other attempts produce simmilar results:
Actual Class: 890
Predicted Class: [890]
Class: 1028 probability: 0.13682366473176108 %
Class: 1107 probability: 0.1372180833047104 %
Class: 1358 probability: 0.09312179238345174 %
Class: 598 probability: 0.11286567433780788 %
Class: 707 probability: 0.07636519076484871 %
Class: 875 probability: 0.07712682059152805 %
Class: 884 probability: 0.1169118710112273 %
Class: 890 probability: 0.12735823672708854 %
Class: 988 probability: 0.12220866614757639 %
Is there anything I can do to get these probabiltiy scores up (or down)? rather than sitting around the 10% - 12% mark for everything I try it on? I tested this on a seperate english language corpus; 9 texts, all about the same size ranging in dates from 900 - 1600, this was giving me very simmilar scores. I need a probability score because part of my project is to see whether a text can be roughly dated based on a range of class simmilarity scores from various dates.