Question: Folks,
greetings from the sunny Spain.
I have build a lightbox.
Pictures attached show the home and clicked image states where you can see whats the issue about.
Im doing everything I know to try to fix it, and I wonder if anybody fancy figuring out what am I doing wrong.
Theres no fancy in a new bee 500 line code that could have been written better I am sure but, would you give it a go and KILL that white margin on the left? (it shows the same on mobile)
[enter image description here](https://i.stack.imgur.com/l2mXI.png)

Answer: 
Possible but not practical.

The aim of transfer learning is to give initial weights to the deep learning (DL) models and speed up the learning process. You can find that given one same DL model, when applied to similar applications such as computer vision, all the generated DL models have relative range of values although not entirely significant but better than randomization of weights or even sparse.

Machine learning (ML) models have shallow architectures and you can simply use randomization of weights to train-test the model.

If you insist to do transfer learning, you can use the weights of the previous model you are referred to but make sure you have same input-output data and configure your model accordingly. You will notice that you cannot find transfer learning for ML anywhere and easier, because it is not practical. Better learn from scratch.
One method to use if you want you are using transfer learning to fit a small target dataset and are not concerned about training time. you could use all or part of the original data then add all of the target data in as well. in addition you could augment the target data to ensure the model is 'more fit' to it. I have done this with light GBM and it works well, so in theory, it should also work fine on a random forest.

Also, I found this thesis that explores the topic in more detail [Transfer Learning Using Decision Forests](https://www.cs.technion.ac.il/users/wwwb/cgi-bin/tr-get.cgi/2016/MSC/MSC-2016-02.pdf) by Noam Segev.

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> In this work we propose three transfer algorithms based on random forests. Two of our algorithms refine a classifier learned on the source set using the available target set, while the last uses both sets directly during tree induction.
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