AI PHP Rubix Wrap
A wrapper for Rubix ML to make it very approachable
Example:
$report = RubixService::train($data, 'column_with_label');
Where column_with_label is the key of the multi dimensional array $data that contains the value that you want to predict.
Let's make a simple example:
$apartment_data = [
['space_m2' => 10, 'price' => 100],
['space_m2' => 20, 'price' => 200],
['space_m2' => 30, 'price' => 300],
['space_m2' => 40, 'price' => 400],
//...
['space_m2' => 280, 'price' => 2800],
['space_m2' => 290, 'price' => 2900],
['space_m2' => 300, 'price' => 3000],
];
$report = RubixService::train($apartment_data, 'price');
var_export($report);
/*
array (
'mean absolute error' => 68.88888888888889,
...
'r squared' => 0.9796739130434783,
...
)
*/
$prediction = RubixService::predict(['space_m2' => 250]);
//$prediciton ~2440
See full example of above code here
Reports / Errors / Accuracy
Mean absolute error is basically the actual error you can expect in average. So in average if trying to predict an apartment given the space, you'd be off, in average, by 68.88$
r squared on the other hand gives more of a feeling how good the algorithm is in %. A high r squared means it works well. For categorical features like cat or dog a different report is returned
Estimators / Machine Learning Algorithm
RubixService::train() will use a default estimator (machine learning algorithm) depending on the data. If you want to choose a different estimator I recommend reading here
rubix ml choosing an estimator
Notice: Neural network is called Multilayer Perceptron in Rubix. Linear regression is called Ridge.
Per default it uses K-d Neighbors or K-d Neighbors Regressor
RubixService::train() takes as well transformers
In detail RubixService:train() does
- shuffle of
$data - train against 70% of
$data - test against 30% of
$data
You can change that behaviour by using the argument train_part_size e.g. if you want to train on 80%, and test on 20% you would do RubixService::train(... train_part_size: 0.8).
5 Jun 13, 2023
13 Nov 18, 2022
65 Oct 14, 2022
20 Dec 8, 2018
192 Nov 28, 2022
9 Oct 27, 2022
4 Nov 30, 2022
8 Jul 15, 2022
33 Nov 19, 2022
1.7k Jan 6, 2023
13 Dec 29, 2022
3 May 30, 2022
1 Dec 1, 2021
812 Dec 30, 2022
795 Dec 21, 2022
5 Jan 13, 2022
118 Dec 28, 2022
13 Apr 10, 2022
8 Dec 28, 2022