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ewis:laboratoare:08 [2023/04/26 17:54] alexandru.predescu [Exercises] |
ewis:laboratoare:08 [2023/04/26 17:55] (current) alexandru.predescu [Exercises] |
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| Download the {{:ewis:laboratoare:lab8:lab8.zip|Project Archive}} and install the required packages via //requirements.txt// | Download the {{:ewis:laboratoare:lab8:lab8.zip|Project Archive}} and install the required packages via //requirements.txt// | ||
| - | === Task 1 (2p) === | + | === Task 1 (1p) === |
| Run //task1.py//: | Run //task1.py//: | ||
| Line 188: | Line 188: | ||
| * The predictions are evaluated to find out the accuracy of the model and the decision tree is then shown as (pseudo)code (if else statements) and graph representation as //dtree1.png//. | * The predictions are evaluated to find out the accuracy of the model and the decision tree is then shown as (pseudo)code (if else statements) and graph representation as //dtree1.png//. | ||
| - | <note important>Change the amount of data used for training the model and evaluate the results: | + | Change the amount of data used for training the model and evaluate the results: |
| * prediction accuracy and generated output | * prediction accuracy and generated output | ||
| * how large is the decision tree regarding the number of leaf nodes? | * how large is the decision tree regarding the number of leaf nodes? | ||
| - | </note> | ||
| === Task 2 (2p) === | === Task 2 (2p) === | ||
| Line 201: | Line 200: | ||
| * The results are plotted on a chart, showing the effect of the amount (percent) of training data on the prediction accuracy | * The results are plotted on a chart, showing the effect of the amount (percent) of training data on the prediction accuracy | ||
| - | <note important>Evaluate the results: | + | Evaluate the results: |
| * How much training data (percent) is required in this case to obtain the most accurate predictions? | * How much training data (percent) is required in this case to obtain the most accurate predictions? | ||
| - | </note> | ||
| === Task 3 (3p) === | === Task 3 (3p) === | ||
| Line 230: | Line 228: | ||
| </code> | </code> | ||
| - | <note important>Use //n_train_percent// to change the amount of data used for training the model and evaluate the results: | + | Use //n_train_percent// to change the amount of data used for training the model and evaluate the results: |
| * prediction accuracy and generated output | * prediction accuracy and generated output | ||
| * how large is the decision tree regarding the number of leaf nodes? | * how large is the decision tree regarding the number of leaf nodes? | ||
| - | </note> | ||
| - | === Task 4 === | + | === Task 4 (4p) === |
| Create //task4.py//: | Create //task4.py//: | ||
| Line 242: | Line 239: | ||
| * Run //task4.py// for both red (//winequality_red.csv//) and white (//winequality_white.csv//) wine datasets | * Run //task4.py// for both red (//winequality_red.csv//) and white (//winequality_white.csv//) wine datasets | ||
| - | <note important>Evaluate the results: | + | Evaluate the results: |
| * How much training data (percent) is required in this case to obtain the most accurate predictions? | * How much training data (percent) is required in this case to obtain the most accurate predictions? | ||
| * What is the average accuracy for each model (decision tree, random forest) | * What is the average accuracy for each model (decision tree, random forest) | ||
| - | </note> | ||
| /* | /* | ||
