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I have produced a large heatmap-like confusion matrix and am seeing horizontal and vertical lines on it, so I'm trying to determine:

  1. What they mean
  2. Why they are there
  3. How I can improve on this

Confusion Matrix Heatmap


Approach

I am relatively new to ML and in the early stages of of a multi-class text classification problem. I may be a little verbose so you can ensure I'm on track and my question isn't due to a flaw in my approach.

I have 90,000+ samples that I'd like to be able to classify into one of 412 classes. I've taken a basic look at the data in terms of its class distribution and the unigrams and bigrams that are selected for each class. Continuing exploration, I trained 4 classifiers on the data, receiving the following levels of accuracy:

LinearSVC                 0.547190
LogisticRegression        0.530063
MultinomialNB             0.368121
RandomForestClassifier    0.200568

Having had a lot of trouble plotting a confusion matrix this large with Seaborn or Matplotlib, I used used the following python code to produce a confusion matrix in CSV:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC

def make_confusion_matrix(a,p,c):
    cm = pd.DataFrame(0,index=c,columns=c)
    for count in range(len(p)):
        cm[int(a[count])][int(p[count])]+=1
    return cm

tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', encoding='latin-1', ngram_range=(1, 2), stop_words='english')
features = tfidf.fit_transform(df['DetailedDescription'])

model = LinearSVC()
X_train, X_test, y_train, y_test, indices_train, indices_test = train_test_split(features, df['BreakdownAgency'], df.index, test_size=0.33, random_state=0)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)    

cm = make_confusion_matrix(y_test.tolist(),y_pred,labels_df['TOOCS Breakdown Agency'])
cm.to_csv('ConfusionMatrix.csv')

I was finally able to view the confusion matrix in a heatmap style by using Excel conditional formatting, which produced the matrix above.

Interpretation

Given that the X axis is actual and y axis is predicted:

I interpret the horizontal lines as showing incorrect bias of predictions towards a class with a disproportionately large number of samples?

I interpret the vertical lines as showing incorrect predictions away from a class with a disproportionately large number of samples?

Does this show that the model is both overfitting and underfitting the data? Or that the samples within my classes are overly diverse?

Action

I'm contemplating:

  1. Manually adding samples to the classes that have very few (a minimum of 10?).
  2. Using SMOTE to oversample small classes (knn=6).
  3. Potentially removing some samples that are atypical or incorrect.

Any help on my Interpretation or Action would be greatly appreciated!

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If the $x$-axis is actual and the $y$-axis is predicted, then vertical lines means a given input of the class $x$ is not being discriminated sufficiently and as a result is randomly mapped to a number of classes. Whereas a horizontal line signifies that many classes are mapped as belonging to a particular class.

Class imbalance

Many reasons can lead to these lines in your confusion matrix. The most common is class imbalance. If you train your model using data that is not well balanced across the different classes this will result in both horizontal and vertical lines. Consider a model trained to distinguish cats, dogs and foxes with 1000, 1000, and 10 instances each. Evidently, you can see how many future instances of foxes may be classified as dogs or cats.

You can remedy this by obtaining more data. This is often not possible, so you can try and weight the model to account for the class imbalance. You can do this by weighting the loss function. Or by oversampling the underrepresented class. Furthermore, you can try and synthesize new instances of the underrepresented class. This can be done with a GAN (hard, requires lots of data, unstable), or you can use k-NN type techniques to find out where the data is concentrated and add novel points to add density to this set. The new technique SMOTE is also highly recommended for its simplicity.

Overlapping distributions

Another big reason is a distribution that overlaps. This is when a class' distribution encompasses one or more class distributions. For example, if you are classifying between cats, dogs, shiba inu, corgi. You can see that any amount of training will always cause conflicts between the class dog and shiba inu, corgi. Since these are dogs.

This problem is easy to distinguish for human perceptive classification, like images, but often times we are working with data where the span of these distributions is not as obvious. You can use clustering methods to see if certain classes fall within the distribution cloud of others. I use percent overlap as a metric for this task.

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  • 1
    $\begingroup$ In the end, I determined that the Machine Learning problem I was trying to solve was probably not linear enough for me to solve using these methods. I've been having far better success with a non-linear solution (CNN) with word embeddings. $\endgroup$ – QA Collective Jun 29 '18 at 3:24

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