New answers tagged

0 votes

How do I deal with unbalance classes in a stock market prediction problem?

There is a perfect mapping between the rule and the decision. You know the percentage change; now apply your rule to map that to a buy/sell/hold decision. There is no machine learning to do, but even ...
user avatar
  • 2,830
1 vote
Accepted

What are some methods to reduce a dataframe so I can pass it as one sample to an SVM?

SVM are not meant to solve "arbitrarily long" classification problem, therefore you have few choices: use PCA for sequences, however it takes very long since it has to build a giant matrix ...
user avatar
0 votes

What are some methods to reduce a dataframe so I can pass it as one sample to an SVM?

The standard is to take the mean across each sentence, leaving you with a vector for each participant. From there you can use t-SNE, UMAP or PCA to reduce the size of each vector.
user avatar
0 votes

Why do we take +1. -1 for support vector hyperplane in SVM?

This is an excellent question and one I struggled with as well. Firstly, the margin is not fixed. As your diagram shows, the margin $m = \frac{2}{\lVert w \rVert}$, which is a function of the 2-norm ...
user avatar
0 votes

SVDD vs once Class SVM

The one-class support vector machines (OCSVM) is a one-class classification technique similar to the SVDD. Instead of obtaining a bounding hypersphere around the training data, the OCSVM algorithm ...
user avatar
2 votes
Accepted

Why would we add regularization loss to the gradient itself in an SVM?

The l2 regularization term is being added to the loss itself. But then you need to find the gradient of this new loss; since gradients are additive, this is the same as the gradient of the unpenalized ...
user avatar
  • 9,591
0 votes

Training data in sentiment analysis

Depending on the language of the tweets you collected, and the availability of pre-trained sentiment analysis models for this language. You should aim for models trained on the most similar domains, ...
user avatar
0 votes

Which algorithm is used in sklearn SGDClassifier when modified huber loss is used?

The modified Huber loss is equivalent to a quadratically smoothed SVM with gamma = 2. See also https://www.quora.com/What-algorithm-is-used-in-sklearn%E2%80%99s-SGDClassifier-when-a-modified-huber-...
user avatar

Top 50 recent answers are included