xlearn | High performance , easy-to-use , and scalable machine learning | Machine Learning library
kandi X-RAY | xlearn Summary
kandi X-RAY | xlearn Summary
xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM), all of which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning problems on large-scale sparse data. Many real world datasets deal with high dimensional sparse feature vectors like a recommendation system where the number of categories and users is on the order of millions. In that case, if you are the user of liblinear, libfm, and libffm, now xLearn is your another better choice.
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QUESTION
the xlearn predict function gives a different mse than what you get by looking at the predictions and calculating it yourself. Here is code to do this; you can run it by cloning the xlearn repository and copying the below code in demo/regression/house_price
in the repository
ANSWER
Answered 2021-Feb-09 at 23:58A lot of people use 1/2 MSE for the loss because it makes the derivative "easier". Given that they use the word "loss" rather than "MSE" or something like that, I'd bet this is what's going on.
For clarity, if your loss is
1/2n * [(y_1 - p_1)^2 + ... + (y_n - p_n)^2]
then the derivative (wrt p) would be
-1/n * [(y_1 - p_1) + ... + (y_n - p_n)]
The 2 goes away because you end up multiplying by 2 for the power rule.
pardon the formatting... I don't know how to do math stuff here.
QUESTION
I am trying to write a custom function to do logistic regression-based ML with the caTools
package, but I keep getting the error: undefined columns selected
.
I checked the input to xlearn
and ylearn
arguments to the logit_boost
function and, as explained in the documentation, they are respectively dataframe containing feature and a vector of labels. So not sure what I am doing wrong.
ANSWER
Answered 2018-Jun-18 at 14:36In help(LogitBoost)
examples section, Label = iris[, 5]
results in a vector, as expected in the ylearn
argument to LogitBoost()
.
In your code, label_train <- train %>% dplyr::select(.data = ., !!rlang::enquo(x))
results in a data.frame. dplyr
, by design, defaults to drop = FALSE
(and even ignores the argument) when only one column is selected.
We could do:
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