xLearn | A Lua-based framework for vision | Machine Learning library
kandi X-RAY | xLearn Summary
kandi X-RAY | xLearn Summary
what’s in this package ??. torch 5 torch5 provides a matlab-like environment for state-of-the-art machine learning algorithms. it is easy to use and provides a very efficient implementation, thanks to an easy and fast scripting language (lua) and a underlying c implementation. xlearn xlearn is an extension library for torch. it provides dozens of tools/modules for vision, image processing, and machine learning for vision. luaflow luaflow is a unified flow-graph description environment for [beta] vision / image-processing types of applications. one of its primary objectives is to abstract computing platforms, by providing a unified, high-level description flow. xflow a serializing language for luaflow, that allows algorithms to [beta] be imported/exported from/to other software frameworks. neuflow neuflow is the compiler toolkit for the neuflow processor, developped at new york university / yale university. the neuflow processor is dataflow computer optimized for vision and bio-inspired models of vision. the neuflow compiler currently converts xlearn/torch algorithms to native neuflow’s bytecode. soon to appear is a luaflow>neuflow compiler, which would simplify retargetting. it is quite important to have access to a neuflow device to be able to experiment with
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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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