تمرین یادگیری ماشین (machine learning ) مربوط به بخش Ensemble Learning and Random Forests

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فایل داده ها exel و همچنین فایل سوال با پسوند ipynb برای تمرین داده شده ضمیمه شده است و می بایستی جواب  ها داخل فایل ipynb در قسمتی که نوشته شده  " ENTER CODE HERE " پر و اجرا شود. این تمرین ماشین لرنینگ مربوط به بخش Ensemble Learning and Random Forests می باشد. همچنین شکل ها بعد از کد نویسی می بایستی مطابق شکل های داده شده در تمرین باشد.

در زیر اسم تمرین و خلاصه ای از آن آمده است:

Exploring Tree-Based Regression Methods for 3D Sinusoidal Data

The main purpose of this assignment is for you to gain experience using tree-based methods to solve simple regression problems. In this assignment, you will fit a Gradient-Boosted Regression Tree, a Random Forest, and a Decision Tree to a noisy 3D sinusoidal data set. Since these models can be trained very quickly on the supplied data, I want you to first manually adjust hyperparameter values and observe their influence on the model's predictions. That is, you should manually sweep the hyperparameter space and try to hone in on the optimal hyperparameter values, again, manually. (Yep, that means guess-and-check: pick some values, train the model, observe the prediction curve, repeat.)

But wait, there's more! Merely attempting to identify the optimal hyperparameter values is not enough. Be sure to really get a visceral understanding of how altering a hyperparameter in turn alters the model predictions (i.e. the prediction curve). This is how you will build your machine learning intuition!

So, play around and build some models. When you are done playing with hyperparameter values, you should try to set these values to the optimal values manually (you're likely going to be way off). Then, retrain the model. Next in this assignment, we will perform several grid searches, so you'll be able to compare your "optimal" hyperparameter values with those computed from the grid search.

We will visualize model predictions for the optimal Gradient-Boosted Regression Tree, a Random Forest, and Decision Tree models that were determined by the grid searches. Next, you will compute the generalization error on the test set for the three models.

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