Description
Q1 (Linear Regression):
Use the python library (sklearn.linear model) to train a linear regression model for the
Boston housing dataset:
https://towardsdatascience.com/linear-regression-on-boston-housing-dataset-f409b7e4a155.
Split the dataset to a training set (70% samples) and a testing set (30% samples). Report the root mean
squared errors (RMSE) on the training and testing sets.
Q2 Implement the following five algorithms to train a linear regression model for the Boston housing data set
https://towardsdatascience.com/linear-regression-on-boston-housing-dataset-f409b7e4a155
Split the dataset to a training set (70% samples) and a testing set (30% samples). Report the root mean
squared errors (RMSE) on the training and testing sets.
1. The gradient descent algorithm
2. The stochastic gradient descent (SGD) algorithm
3. The SGD algorithm with momentum
4. The SGD algorithm with Nesterov momentum
5. The AdaGrad algorithm
Q3 (Logistic Regression):
Use the python library (sklearn.linear model) to train a logistic regression model for
the Titanic dataset:
https://blog.goodaudience.com/machine-learning-using-logistic-regression-in-python-with-code-ab3c7f5f3bed.
Split the dataset to a training set (80% samples) and a testing set (20% samples). Report the overall
classification accuracies on the training and testing sets and report the precision, recall, and F-measure scores
for each of the two classes on the training and testing sets.
Q4 (Logistic Regression): Implement the following five algorithms to train a logistic regression model for the
Titantic dataset:
https://blog.goodaudience.com/machine-learning-using-logistic-regression-in-python-with-code-ab3c7f5f3bed.
Split the dataset to a training set (80% samples) and a testing set (20% samples). Report the overall
classification accuracies on the training and testing sets and report the precision, recall, and F-measure scores
for each of the two classes on the training and testing sets.
1. The gradient descent algorithm
2. The stochastic gradient descent (SGD) algorithm
3. The SGD algorithm with momentum
4. The SGD algorithm with Nesterov momentum
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5. The AdaGrad algorithm
Q4 (Bonus Question):
You will get an additional full point (1.0) if you can answer this bonus question correctly.
That means, if you answer Q1-Q4 correctly, you get a full point (1.0) for this HW assignment. If you can
answer Q1-Q5 correctly, you will get 2.0 points.
1. Implement the Adam algorithm to train a linear regression model for the Boston housing data set
https://towardsdatascience.com/linear-regression-on-boston-housing-dataset-f409b7e4a155
Split the dataset to a training set (70% samples) and a testing set (30% samples). Report the root mean
squared errors (RMSE) on the training and testing sets.
2. Implement the Adam algorithm to train a logistic regression model for the Titantic dataset:
https://blog.goodaudience.com/machine-learning-using-logistic-regression-in-python-with-code-ab3c7f5f3bed.
Split the dataset to a training set (80% samples) and a testing set (20% samples). Report the overall
classification accuracies on the training and testing sets and report the precision, recall, and F-measure
scores for each of the two classes on the training and testing sets.
2