本次美国代写是Python机器学习相关的一个assignment

**a. Linearly separable case:**

(1) Use Python to generate a 2D (𝒙𝑖 ∈ ℛ2) linearly separable data set with 50 positive

instances and 50 negative instances and create a scatter plot to visualize the data

set. Split the data set into training set (70%) and test set (30%).

(2) Use Python (not sklearn package) to create the Batch Perceptron training algorithm

and use the training data set in a.(1) to train a Perceptron model. Plot the error

function curve when the training process converges. Create a plot that shows the

training instances and the learnt decision boundary.

(3) Use the test set in a.(1) to test the trained model in a.(2) and show the accuracy of

the trained model.

(4) Use Python (not sklearn package) to create the sequential Perceptron training

algorithm and use the training data set in a.(1) to train a Perceptron model. Plot the

error function curve when the training process converges. Create a plot that shows

the training instances and the resulting decision boundary.

(5) Use the test set in a.(1) to test the trained model in a. (4) and show the accuracy of

the trained model.

(6) Show how you select learning rate during the training process of a.(2) or a.(4) and

demonstrate how the choice of the learning rate is affecting the convergence of the

training process.

(7) Use the Perceptron model in sklearn package to work on the synthetic data set in a.

(1) and compare the results with that in a. (3) and a. (5).

b. Nonlinearly separable case:

(1) Use Python to generate a 2D not linearly separable data set with 50 positive

instances and 50 negative instances and create a scatter plot to visualize the data

set.

(2) Modify the training algorithm you developed in a.(2) to have a training algorithm

that works on a nonseparable data set. Use the synthetic data set in b.(1) to test

your algorithm. Show the error function curve. Plot the decision boundary on the

scatter plot of the data set.

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