I'm never sure when to use one-hot encoding for non-ordered categorical variables and when not to. I use it whenever the algorithm uses a distance metric to compute similarity. Can anyone give a general rule of thumb as to what types of algorithms would require non-ordered categorical features to be one-hot-encoded and which ones wouldn't?
Solved – What algorithms require one-hot encoding
categorical datacategorical-encodingdata preprocessingmachine learning
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This depends on the models (and maybe even software) you want to use. With linear regression, or generalized linear models estimated by maximum likelihood (or least squares) (in R this means using functions lm or glm), you need to leave out one column. Otherwise you will get a message about some columns "left out because of singularities"$^\dagger$.
But if you estimate such models with regularization, for example ridge, lasso er the elastic net, then you should not leave out any columns. The regularization takes care of the singularities, and more important, the prediction obtained may depend on which columns you leave out. That will not happen when you do not use regularization$^\ddagger$. See the answer at How to interpret coefficients of a multinomial elastic net (glmnet) regression which supports this view (with a direct quote from one of the authors of glmnet).
With other models, use the same principles. If the predictions obtained depends on which columns you leave out, then do not do it. Otherwise it is fine.
So far, this answer only mentions linear (and some mildly non-linear) models. But what about very non-linear models, like trees and randomforests? The ideas about categorical encoding, like one-hot, stems mainly from linear models and extensions. There is little reason to think that ideas derived from that context should apply without modification for trees and forests! for some ideas see Random Forest Regression with sparse data in Python.
$^\dagger$ But, using factor variables, R will take care of that for you.
$^\ddagger$ Trying to answer extra question in comment: When using regularization, most often iterative methods are used (as with lasso or elasticnet) which do not need matrix inversion, so that the design matrix do not have full rank is not a problem. With ridge regularization, matrix inversion may be used, but in that case the regularization term added to the matrix before inversion makes it invertible. That is a technical reason, a more profound reason is that removing one column changes the optimization problem, it changes the meaning of the parameters, and it will actually lead to different optimal solutions. As a concrete example, say you have a categorical variable with three levels, 1,2 and 3. The corresponding parameters is $\beta_, \beta_2, \beta_3$. Leaving out column 1 leads to $\beta_1=0$, while the other two parameters change meaning to $\beta_2-\beta_1, \beta_3-\beta_1$. So those two differences will be shrinked. If you leave out another column, other contrasts in the original parameters will be shrinked. So this changes the criterion function being optimized, and there is no reason to expect equivalent solutions! If this is not clear enough, I can add a simulated example (but not today).
One hot encoding would be a preliminary step toward dummy coding or effect coding or any other parameterization of a categorical variable. I don't know anything about scikit-learn (and questions about code are off topic here) but statistical programs such as SAS, R, SPSS, etc. do this encoding for you. It simply takes a single column of labels and turns it into k columns of 0's and 1's where there are k different labels.
You then have to choose what parameterization you want and which label you would like to use as your reference category. This has been discussed here before and will also be covered in any basic regression book.
Best Answer
Most algorithms (linear regression, logistic regression, neural network, support vector machine, etc.) require some sort of the encoding on categorical variables. This is because most algorithms only take numerical values as inputs.
Algorithms that do not require an encoding are algorithms that can directly deal with joint discrete distributions such as Markov chain / Naive Bayes / Bayesian network, tree based, etc.
Additional comments:
One hot encoding is one of the encoding methods. Here is a good resource for categorical variable encoding (not limited to R). R LIBRARY CONTRAST CODING SYSTEMS FOR CATEGORICAL VARIABLES
Even without encoding, distance between data points with discrete variables can be defined, such as hamming distance or Levenshtein Distance