Why do Tensorflow tf.learn classification results

Question

I use the TensorFlow high-level API tf.learn to train and evaluate a DNN classifier for a series of binary text classifications (actually I need multi-label classification but at the moment I check every label separately). My code is very similar to the tf.learn Tutorial

classifier = tf.contrib.learn.DNNClassifier(
    hidden_units=[10],
    n_classes=2,
    dropout=0.1,
    feature_columns=tf.contrib.learn.infer_real_valued_columns_from_input(training_set.data))
classifier.fit(x=training_set.data, y=training_set.target, steps=100)
val_accuracy_score = classifier.evaluate(x=validation_set.data, y=validation_set.target)["accuracy"]

Accuracy score varies roughly from 54% to 90%, with 21 documents in the validation (test) set which are always the same.

What does the very significant deviation mean? I understand there are some random factors (eg. dropout), but to my understanding the model should converge towards an optimum.

I use words (lemmas), bi- and trigrams, sentiment scores and LIWC scores as features, so I do have a very high-dimensional feature space, with only 28 training and 21 validation documents. Can this cause problems? How can I consistently improve the results apart from collecting more training data?

Update: To clarify, I generate a dictionary of occurring words and n-grams and discard those that occur only 1 time, so I only use words (n-grams) that exist in the corpus.

Answer 1

This has nothing to do with TensorFlow. This dataset is ridiculously small, thus you can obtain any results. You have 28 + 21 points, in a space which has "infinite" amount of dimensions (there are around 1,000,000 english words, thus 10^18 trigrams, however some of them do not exist, and for sure they do not exist in your 49 documents, but still you have at least 1,000,000 dimensions). For such problem, you have to expect huge variance of the results.

How can I consistently improve the results apart from collecting more training data?

You pretty much cannot. This is simply way to small sample to do any statistical analysis.

Consequently the best you can do is change evaluation scheme instead of splitting data to 28/21 do 10-fold cross validation, with ~50 points this means that you will have to run 10 experiments, each with 45 training documents and 4 testing ones, and average the result. This is the only thing you can do to reduce the variance, however remember that even with CV, dataset so small gives you no guarantees how well your model will actualy behave "in the wild" (once applied to never seen before data).