Hi,

this is a question on Artificial intelligence, it involves coding.

Is it possible to give the complete solution and code for this problem.

Thankyou

the feature vector with that would tix this problem. In this problem, we will build a binary linear classifier that reads physician notes and guesses whether the patients are having heart problems (+1) or not (-1). In this problem, you will use the test and train files provided for you (train.csv and test.csv). Also, you must implement the functions without using libraries like Scikit-learn a) Suppose that we will use a predictor that takes note x and returns sign(w (x)). Suppose that we wish to use the hinge loss. Write out the expression for Losshinge (X, y, b) Compute the gradient of the loss. c) Assuming y 1, what is the largest magnitude that the gradient can take? That is, find a way to set w to make IIVLoss (x, y, w)ll as big as possible. Leave your answer in terms of (x)11 d) Implement the function extractWordFeatures, which takes a note (string) as input and returns a feature vector (x) (represent the vector (x) as a dictionary in Python) the feature vector with that would tix this problem. In this problem, we will build a binary linear classifier that reads physician notes and guesses whether the patients are having heart problems (+1) or not (-1). In this problem, you will use the test and train files provided for you (train.csv and test.csv). Also, you must implement the functions without using libraries like Scikit-learn a) Suppose that we will use a predictor that takes note x and returns sign(w (x)). Suppose that we wish to use the hinge loss. Write out the expression for Losshinge (X, y, b) Compute the gradient of the loss. c) Assuming y 1, what is the largest magnitude that the gradient can take? That is, find a way to set w to make IIVLoss (x, y, w)ll as big as possible. Leave your answer in terms of (x)11 d) Implement the function extractWordFeatures, which takes a note (string) as input and returns a feature vector (x) (represent the vector (x) as a dictionary in Python)