Executive Solution Overview
– ## MMIS671_homework21.docx – hw2-1.q2.data1.zip **## MMIS671_homework21.docx** **Homework 2.** **Question 1. Decision Tree Classifier [10 Points]** Data: The zip file “ _hw2.q1.data.zip_” contains 3 CSV files: · “ _hw2.q1.train.csv_” contains 10,000 rows and 26 columns. The first column ‘ _y_’…
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– ## MMIS671_homework21.docx
– hw2-1.q2.data1.zip
**## MMIS671_homework21.docx**
**Homework 2.**
**Question 1. Decision Tree Classifier [10 Points]**
Data: The zip file “
_hw2.q1.data.zip_” contains 3 CSV files:
· “
_hw2.q1.train.csv_” contains 10,000 rows and 26 columns. The first column ‘
_y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
· “
_hw2.q1.test.csv_” contains 2,000 rows and 26 columns. The first column ‘
_y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
· “
_hw2.q1.new.csv_” contains 30 rows and 26 columns. The first column ‘
_ID_’ is an identifier for 30 unlabeled samples. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
**Task 1**. [4 points]
Use 5-fold cross-validation with the 10,000 labeled exampled from “
_hw2.q1.train.csv_” to determine the fewest number of rules using which a decision tree classifier can achieve mean cross-validation accuracy of at least 0.96. Report the number of rules needed, the cross-validation accuracy obtained, and all the hyper-parameter values for the
_DecisionTreeClassifier_.
_Fewest number of rules needed_: ………………. (to achieve mean cross-validation accuracy of at least 0.96)
_Mean cross-validation accuracy: ………………………._(
_rounded to 4 decimal places_)
_Non-default hHyper-parameter values for selected DecisionTreeClassifier model:_
**Task 2**. [2 Points]
Train a
_DecisionTreeClassifier_ with the hyper-parameter values determined in Task 1 on all 10,000 training samples and use it to predict the output class ‘
_y_’ for the 2,000 examples in “
_hw2.q1.test.csv_”
_._ Report the following:
·
_Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)
·
_Classification report_ for the 2,000 test examples:
·
_Confusion matrix_ for the 2,000 test examples:
**Task 3.**[2 Points]
Use the model trained in Task 2 to predict the output class ‘
_y’_ for the 30 examples in “
_hw2.q1.new.csv_”. Specify the predicted classes in the table below:
| | |
| — | — |
| ID | predicted y |
| 1 | |
| 2 | |
| 3 | |
| 4 | |
| 5 | |
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| 30 | |
**Task 4.**[2 Points]
Of the 25 input variables which ones are relevant for this classification task?
The following … input variables are relevant for this classification task: …………………
Display your trained decision tree:
**Question 2. Supervised machine learning classifiers [10 Points]**
Data: The zip file “
_hw2.q2.data.zip_” contains 3 CSV files:
· “
_hw2.q2.train.csv_” contains 8,000 rows and 11 columns. The first column ‘
_y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
· “
_hw2.q2.test.csv_” contains 2,000 rows and 11 columns. The first column ‘
_y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
· “
_hw2.q1.new.csv_” contains 30 rows and 10 columns. The first column ‘
_ID_’ is an identifier for 30 unlabeled samples. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
**Task 1**. [6 points]
Use 4-fold cross-validation with the 8,000 labeled exampled from “
_hw2.q2.train.csv_” to identify a classifier that achieves mean cross-validation accuracy of at least 0.96. You should try several
_Scikit-Learn_ classifiers, including:
_GaussianNB, DecisionTreeClassifier, RandomForestClassifier, ExtraTreesClassifier, KNeighborsClassifier, LogisticRegression, SVC, and MLPClassifier_. Try different hyper-parameter values for the better performing classifiers to obtain a good set of hyper-parameter values. Then select the best performing model. Report the following:
**_Selected model with hyper-parameter values_** _:_
_Mean cross-validation accuracy: ………………………._(
_rounded to 4 decimal places_)
**Task 2**. [2 Points]
Train the classifier with the hyper-parameter values determined in Task 1 on all 8,000 training samples and use it to predict the output class ‘
_y_’ for the 2,000 examples in “
_hw2.q2.test.csv_”
_._ Report the following:
·
_Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)
·
_Classification report_ for the 2,000 test examples:
·
_Confusion matrix_ for the 2,000 test examples:
**Task 3.**[2 Points]
Use the model trained in Task 2 to predict the output class ‘
_y’_ for the 30 examples in “
_hw2.q2.new.csv_”. Specify the predicted classes in the table below:
| | |
| — | — |
| ID | predicted y |
| ID_001 | |
| ID_002 | |
| ID_003 | |
| ID_004 | |
| ID_005 | |
| ID_006 | |
| ID_007 | |
| ID_008 | |
| ID_009 | |
| ID_010 | |
| ID_011 | |
| ID_012 | |
| ID_013 | |
| ID_014 | |
| ID_015 | |
| ID_016 | |
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| ID_019 | |
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| ID_021 | |
| ID_022 | |
| ID_023 | |
| ID_024 | |
| ID_025 | |
| ID_026 | |
| ID_027 | |
| ID_028 | |
| ID_029 | |
| ID_030 | |
**hw2-1.q2.data1.zip**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/hw2-1-q2-data1-zip)
**hw2-1.q2.data1.zip**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/hw2-1-q2-data1-zip)
**## MMIS671_homework21.docx**
**Homework 2.**
**Question 1. Decision Tree Classifier [10 Points]**
Data: The zip file “
_hw2.q1.data.zip_” contains 3 CSV files:
· “
_hw2.q1.train.csv_” contains 10,000 rows and 26 columns. The first column ‘
_y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
· “
_hw2.q1.test.csv_” contains 2,000 rows and 26 columns. The first column ‘
_y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
· “
_hw2.q1.new.csv_” contains 30 rows and 26 columns. The first column ‘
_ID_’ is an identifier for 30 unlabeled samples. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
**Task 1**. [4 points]
Use 5-fold cross-validation with the 10,000 labeled exampled from “
_hw2.q1.train.csv_” to determine the fewest number of rules using which a decision tree classifier can achieve mean cross-validation accuracy of at least 0.96. Report the number of rules needed, the cross-validation accuracy obtained, and all the hyper-parameter values for the
_DecisionTreeClassifier_.
_Fewest number of rules needed_: ………………. (to achieve mean cross-validation accuracy of at least 0.96)
_Mean cross-validation accuracy: ………………………._(
_rounded to 4 decimal places_)
_Non-default hHyper-parameter values for selected DecisionTreeClassifier model:_
**Task 2**. [2 Points]
Train a
_DecisionTreeClassifier_ with the hyper-parameter values determined in Task 1 on all 10,000 training samples and use it to predict the output class ‘
_y_’ for the 2,000 examples in “
_hw2.q1.test.csv_”
_._ Report the following:
·
_Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)
·
_Classification report_ for the 2,000 test examples:
·
_Confusion matrix_ for the 2,000 test examples:
**Task 3.**[2 Points]
Use the model trained in Task 2 to predict the output class ‘
_y’_ for the 30 examples in “
_hw2.q1.new.csv_”. Specify the predicted classes in the table below:
| | |
| — | — |
| ID | predicted y |
| 1 | |
| 2 | |
| 3 | |
| 4 | |
| 5 | |
| 6 | |
| 7 | |
| 8 | |
| 9 | |
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| 28 | |
| 29 | |
| 30 | |
**Task 4.**[2 Points]
Of the 25 input variables which ones are relevant for this classification task?
The following … input variables are relevant for this classification task: …………………
Display your trained decision tree:
**Question 2. Supervised machine learning classifiers [10 Points]**
Data: The zip file “
_hw2.q2.data.zip_” contains 3 CSV files:
· “
_hw2.q2.train.csv_” contains 8,000 rows and 11 columns. The first column ‘
_y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
· “
_hw2.q2.test.csv_” contains 2,000 rows and 11 columns. The first column ‘
_y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
· “
_hw2.q1.new.csv_” contains 30 rows and 10 columns. The first column ‘
_ID_’ is an identifier for 30 unlabeled samples. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
**Task 1**. [6 points]
Use 4-fold cross-validation with the 8,000 labeled exampled from “
_hw2.q2.train.csv_” to identify a classifier that achieves mean cross-validation accuracy of at least 0.96. You should try several
_Scikit-Learn_ classifiers, including:
_GaussianNB, DecisionTreeClassifier, RandomForestClassifier, ExtraTreesClassifier, KNeighborsClassifier, LogisticRegression, SVC, and MLPClassifier_. Try different hyper-parameter values for the better performing classifiers to obtain a good set of hyper-parameter values. Then select the best performing model. Report the following:
**_Selected model with hyper-parameter values_** _:_
_Mean cross-validation accuracy: ………………………._(
_rounded to 4 decimal places_)
**Task 2**. [2 Points]
Train the classifier with the hyper-parameter values determined in Task 1 on all 8,000 training samples and use it to predict the output class ‘
_y_’ for the 2,000 examples in “
_hw2.q2.test.csv_”
_._ Report the following:
·
_Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)
·
_Classification report_ for the 2,000 test examples:
·
_Confusion matrix_ for the 2,000 test examples:
**Task 3.**[2 Points]
Use the model trained in Task 2 to predict the output class ‘
_y’_ for the 30 examples in “
_hw2.q2.new.csv_”. Specify the predicted classes in the table below:
| | |
| — | — |
| ID | predicted y |
| ID_001 | |
| ID_002 | |
| ID_003 | |
| ID_004 | |
| ID_005 | |
| ID_006 | |
| ID_007 | |
| ID_008 | |
| ID_009 | |
| ID_010 | |
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| ID_012 | |
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| ID_019 | |
| ID_020 | |
| ID_021 | |
| ID_022 | |
| ID_023 | |
| ID_024 | |
| ID_025 | |
| ID_026 | |
| ID_027 | |
| ID_028 | |
| ID_029 | |
| ID_030 | |
**hw2-1.q2.data1.zip**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/hw2-1-q2-data1-zip)
**## MMIS671_homework21.docx**
**Homework 2.**
**Question 1. Decision Tree Classifier [10 Points]**
Data: The zip file “
_hw2.q1.data.zip_” contains 3 CSV files:
· “
_hw2.q1.train.csv_” contains 10,000 rows and 26 columns. The first column ‘
_y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
· “
_hw2.q1.test.csv_” contains 2,000 rows and 26 columns. The first column ‘
_y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
· “
_hw2.q1.new.csv_” contains 30 rows and 26 columns. The first column ‘
_ID_’ is an identifier for 30 unlabeled samples. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
**Task 1**. [4 points]
Use 5-fold cross-validation with the 10,000 labeled exampled from “
_hw2.q1.train.csv_” to determine the fewest number of rules using which a decision tree classifier can achieve mean cross-validation accuracy of at least 0.96. Report the number of rules needed, the cross-validation accuracy obtained, and all the hyper-parameter values for the
_DecisionTreeClassifier_.
_Fewest number of rules needed_: ………………. (to achieve mean cross-validation accuracy of at least 0.96)
_Mean cross-validation accuracy: ………………………._(
_rounded to 4 decimal places_)
_Non-default hHyper-parameter values for selected DecisionTreeClassifier model:_
**Task 2**. [2 Points]
Train a
_DecisionTreeClassifier_ with the hyper-parameter values determined in Task 1 on all 10,000 training samples and use it to predict the output class ‘
_y_’ for the 2,000 examples in “
_hw2.q1.test.csv_”
_._ Report the following:
·
_Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)
·
_Classification report_ for the 2,000 test examples:
·
_Confusion matrix_ for the 2,000 test examples:
**Task 3.**[2 Points]
Use the model trained in Task 2 to predict the output class ‘
_y’_ for the 30 examples in “
_hw2.q1.new.csv_”. Specify the predicted classes in the table below:
| | |
| — | — |
| ID | predicted y |
| 1 | |
| 2 | |
| 3 | |
| 4 | |
| 5 | |
| 6 | |
| 7 | |
| 8 | |
| 9 | |
| 10 | |
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| 22 | |
| 23 | |
| 24 | |
| 25 | |
| 26 | |
| 27 | |
| 28 | |
| 29 | |
| 30 | |
**Task 4.**[2 Points]
Of the 25 input variables which ones are relevant for this classification task?
The following … input variables are relevant for this classification task: …………………
Display your trained decision tree:
**Question 2. Supervised machine learning classifiers [10 Points]**
Data: The zip file “
_hw2.q2.data.zip_” contains 3 CSV files:
· “
_hw2.q2.train.csv_” contains 8,000 rows and 11 columns. The first column ‘
_y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
· “
_hw2.q2.test.csv_” contains 2,000 rows and 11 columns. The first column ‘
_y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
· “
_hw2.q1.new.csv_” contains 30 rows and 10 columns. The first column ‘
_ID_’ is an identifier for 30 unlabeled samples. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
**Task 1**. [6 points]
Use 4-fold cross-validation with the 8,000 labeled exampled from “
_hw2.q2.train.csv_” to identify a classifier that achieves mean cross-validation accuracy of at least 0.96. You should try several
_Scikit-Learn_ classifiers, including:
_GaussianNB, DecisionTreeClassifier, RandomForestClassifier, ExtraTreesClassifier, KNeighborsClassifier, LogisticRegression, SVC, and MLPClassifier_. Try different hyper-parameter values for the better performing classifiers to obtain a good set of hyper-parameter values. Then select the best performing model. Report the following:
**_Selected model with hyper-parameter values_** _:_
_Mean cross-validation accuracy: ………………………._(
_rounded to 4 decimal places_)
**Task 2**. [2 Points]
Train the classifier with the hyper-parameter values determined in Task 1 on all 8,000 training samples and use it to predict the output class ‘
_y_’ for the 2,000 examples in “
_hw2.q2.test.csv_”
_._ Report the following:
·
_Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)
·
_Classification report_ for the 2,000 test examples:
·
_Confusion matrix_ for the 2,000 test examples:
**Task 3.**[2 Points]
Use the model trained in Task 2 to predict the output class ‘
_y’_ for the 30 examples in “
_hw2.q2.new.csv_”. Specify the predicted classes in the table below:
| | |
| — | — |
| ID | predicted y |
| ID_001 | |
| ID_002 | |
| ID_003 | |
| ID_004 | |
| ID_005 | |
| ID_006 | |
| ID_007 | |
| ID_008 | |
| ID_009 | |
| ID_010 | |
| ID_011 | |
| ID_012 | |
| ID_013 | |
| ID_014 | |
| ID_015 | |
| ID_016 | |
| ID_017 | |
| ID_018 | |
| ID_019 | |
| ID_020 | |
| ID_021 | |
| ID_022 | |
| ID_023 | |
| ID_024 | |
| ID_025 | |
| ID_026 | |
| ID_027 | |
| ID_028 | |
| ID_029 | |
| ID_030 | |
**hw2-1.q2.data1.zip**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/hw2-1-q2-data1-zip)
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