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ewis:laboratoare:08 [2022/05/04 18:16]
alexandru.predescu
ewis:laboratoare:08 [2022/05/04 21:47] (current)
alexandru.predescu [Exercises]
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<note important>​Use //​n_train_percent//​ to change the amount of data used for training the model and evaluate the results. Set //​n_train_percent=UCODE//​ and report the results: ​ <note important>​Use //​n_train_percent//​ to change the amount of data used for training the model and evaluate the results. Set //​n_train_percent=UCODE//​ and report the results: ​
* prediction accuracy and generated output //​dtree1.png//​   * prediction accuracy and generated output //​dtree1.png//​
-  * 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>​ </​note>​

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<note important>​Use //​n_train_percent//​ to change the amount of data used for training the model and evaluate the results. Set //​n_train_percent=UCODE//​ and report the results: ​ <note important>​Use //​n_train_percent//​ to change the amount of data used for training the model and evaluate the results. Set //​n_train_percent=UCODE//​ and report the results: ​
* prediction accuracy and generated output //​dtree31.png//​   * prediction accuracy and generated output //​dtree31.png//​
-  * 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>​ </​note>​

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<note important>​Write down your observations regarding the results: <note important>​Write down your observations regarding the results:
-  * How much training data (percent) is required in this case to obtain most accurate predictions+  * How much training data (percent) is required in this case to obtain most accurate predictions?
* What is the average accuracy for each model (decision tree, random forest) **(+1p)**   * What is the average accuracy for each model (decision tree, random forest) **(+1p)**
* Which type of wine (red/white) is easier to predict (more accurate) based on the results **(+1p)**   * Which type of wine (red/white) is easier to predict (more accurate) based on the results **(+1p)**
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Use //​n_train_percent//​ to change the amount of data used for training the model and evaluate the results. Set //​n_train_percent//​ as the generated //code// **(//​UCODE//​)** and report the results: ​ Use //​n_train_percent//​ to change the amount of data used for training the model and evaluate the results. Set //​n_train_percent//​ as the generated //code// **(//​UCODE//​)** and report the results: ​
* prediction accuracy and generated output //​dtree32.png//​   * prediction accuracy and generated output //​dtree32.png//​
-  * how large is the decision tree regarding the number of leaf nodes+  * how large is the decision tree regarding the number of leaf nodes?

Create a new script similar to //​task31_sol.py//​ to compare the decision trees with random forest models using variable amounts (percent) of training data: Create a new script similar to //​task31_sol.py//​ to compare the decision trees with random forest models using variable amounts (percent) of training data:
-  * How much training data (percent) is required in this case to obtain most accurate predictions+  * How much training data (percent) is required in this case to obtain 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)
* Explain the low accuracy obtained for this case study. What would be required to improve the results? **(+2p)**   * Explain the low accuracy obtained for this case study. What would be required to improve the results? **(+2p)**
ewis/laboratoare/08.1651677386.txt.gz · Last modified: 2022/05/04 18:16 by alexandru.predescu 