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ewis:laboratoare:07 [2023/04/19 17:08] alexandru.predescu [Exercises] |
ewis:laboratoare:07 [2023/04/19 18:08] (current) alexandru.predescu [Exercises] |
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| ==== Exercises ==== | ==== Exercises ==== | ||
| - | === Task 1 (5p) === | + | === Task 1 (3p) === |
| Download the {{:ewis:laboratoare:lab7:project_lab7.zip|project archive}} and unzip on your PC. Install the requirements using pip (e.g. //py -3 -m pip install -r requirements.txt//). | Download the {{:ewis:laboratoare:lab7:project_lab7.zip|project archive}} and unzip on your PC. Install the requirements using pip (e.g. //py -3 -m pip install -r requirements.txt//). | ||
| - | The code sample (//task12.py//) uses linear regression to fit a sample of generated data. | + | The script (//task1.py//) uses linear regression to fit a sample of generated data. |
| Run the program and solve the following scenarios: | Run the program and solve the following scenarios: | ||
| * Experiment with different polynomial orders | * Experiment with different polynomial orders | ||
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| * Q3: Explain the results based on the provided function that is used to generate the dataset. | * Q3: Explain the results based on the provided function that is used to generate the dataset. | ||
| - | === Task 2 (5p) === | + | === Task 2 (3p) === |
| - | The code sample (//task3.py//) loads the Boston Housing Dataset and trains a linear model over multiple features. The prediction results (median housing prices in thousands of dollars) are shown in the plot and compared to the original dataset. | + | The script (//task2.py//) loads a dataset from a CSV file. Run a similar script as Task 1, and present your results. |
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| + | [[https://www.kaggle.com/datasets/meetnagadia/bitcoin-stock-data-sept-17-2014-august-24-2021|Bitcoin Price Dataset]] | ||
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| + | === Task 3 (3p) === | ||
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| + | The script (//task3.py//) loads the Boston Housing Dataset and trains a linear model over multiple features. The prediction results (median housing prices in thousands of dollars) are shown in the plot and compared to the original dataset. | ||
| Run the program and solve the following scenarios: | Run the program and solve the following scenarios: | ||
| * [TODO 1] Change the size of the training dataset (percent) and evaluate the models that are obtained in each case using RMSE | * [TODO 1] Change the size of the training dataset (percent) and evaluate the models that are obtained in each case using RMSE | ||
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| * Q2. What is the amount (percent) of training data that provides the best results in terms of prediction accuracy on validation data? | * Q2. What is the amount (percent) of training data that provides the best results in terms of prediction accuracy on validation data? | ||
| * Q3. What happens if the amount training data is small, e.g. 10%, with regards to the prediction accuracy and the over/underfitting of the regression model? | * Q3. What happens if the amount training data is small, e.g. 10%, with regards to the prediction accuracy and the over/underfitting of the regression model? | ||
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| - | Submit your answers on Moodle as PDF report. | ||
| ==== Resources ==== | ==== Resources ==== | ||
| - | * {{:ewis:laboratoare:lab7:project_lab7.zip|Project}} | + | * {{:ewis:laboratoare:lab7:lab7.zip|Project}} |
| * {{:ewis:laboratoare:python_workflow.pdf|Python Workflow}} | * {{:ewis:laboratoare:python_workflow.pdf|Python Workflow}} | ||
| * [[https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html|The Boston Housing Dataset]] | * [[https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html|The Boston Housing Dataset]] | ||
