Search Results

I can imagine this happening if you were combining categories that don't have anything in common, say photos of wedding dresses, excavators, and parrots. In such a case, there is not much that the alg …

Yes, it can be a problem. A very similar example was used in the Unmasking Clever Hans Predictors and Assessing What Machines Really Learn paper by Lapuschkin et al (see below). They show an example o …

“High risk” of what? You are predicting the number of failures, so if you aim to predict if there's a risk of failure, anything close to or higher than 1 failure is a risk. On another hand, if you mea …

Multiclass neural network would differ from multiple binary classifiers by using a different activation functions. You may approach both problems differently, but technically, that's the only change t …

Two simple things people commonly do is: Use one of the many available lists of stopwords and filler them out first. Most NLP software does this out-of-the-box. Decide on the number of words that wil …

There is no default probability cutoff for classifiers. Using 0.5 cutoff is optimal only if you aim at minimizing accuracy (a.k.a. 0-1 loss), and it is a "problematic" and misleading measure of error. …

Start with the The Two Cultures: statistics vs. machine learning? thread. Machine learning is about finding patterns or correlations in data. Causal inference, like statistics, is about inference. As …

In this blog post you can find a review of those metrics, it also mentions the weighted metrics that you use. If you look closely, same as in your case, accuracy and weighted recall are equal in their …

If you have only two classes it doesn't matter what is the classification rule (one-vs-all or mutually exclusive classes) since for two classes they are the same.

I have been dealing with very similar problem recently, the two differences were that the descriptions were shorter and training set was larger. We tried several approaches, including random forest, r …

It's quite simple, using class number 5 as an example, just use take all the samples from class 5 as “positive” instance and all the other classes as “negative” instance, same for predictions. Just ke …

Scikit-learn is open-source so you can check the source code yourself for the differences. If you look at the implementations of statistical algorithms in high-quality software (R, scikit-learn) vs n …