Q2. Logistic Regression: Code [25] In this task, you will learn to build a Logistic Regression...

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Q2. Logistic Regression: Code [25] In this task, you will learn to build a Logistic Regression Classifier for the same "Financial Phrasebank" dataset. Bag of Words model will be used for this task. 1. Use 60% of the data selected randomly for training, 20% selected randomly for testing and the remaining 20% for validation set. Use classes 'positive' and negative' only. Perform the same cleaning tasks on the text data and build a vocabulary of the words. 2. Using CountVectorizer, fit the cleaned train data. This will create the bag-of-words model for the train data. Transform test and validation sets using same CountVectorizer. 3. To implement the logistic regression using following equations, Zi = W.xi = (z) we need the weight vector W. Create an array of dimension equal to those of each x; from the CountVectorizer. 4. Apply above equations over whole training dataset and calculate and cross-entropy loss LCE which can be calculated as LCE y log + (1 y) log(1 - ) 5. Now, update the weights as follows: - W+1 = W (i Yi).Xi Here, (i y;).x; is the gradient of sigmoid function and = 0.01 is the learning rate. 6. Repeat step 4 and step 5 for 500 iterations or epochs. For each iteration, calculate the cross-entropy loss on validation set. 7. Calculate the accuracy and macro-average precision, recall, and F1 score and provide the confusion matrix on the test set. 8. Experiment with varying values of = (0.0001, 0.001, 0.01, 0.1). Report your observations with respect to the performance of the model. You can vary the number of iterations to enhance the performance if necessary. Q2. Logistic Regression: Code [25] In this task, you will learn to build a Logistic Regression Classifier for the same "Financial Phrasebank" dataset. Bag of Words model will be used for this task. 1. Use 60% of the data selected randomly for training, 20% selected randomly for testing and the remaining 20% for validation set. Use classes 'positive' and negative' only. Perform the same cleaning tasks on the text data and build a vocabulary of the words. 2. Using CountVectorizer, fit the cleaned train data. This will create the bag-of-words model for the train data. Transform test and validation sets using same CountVectorizer. 3. To implement the logistic regression using following equations, Zi = W.xi = (z) we need the weight vector W. Create an array of dimension equal to those of each x; from the CountVectorizer. 4. Apply above equations over whole training dataset and calculate and cross-entropy loss LCE which can be calculated as LCE y log + (1 y) log(1 - ) 5. Now, update the weights as follows: - W+1 = W (i Yi).Xi Here, (i y;).x; is the gradient of sigmoid function and = 0.01 is the learning rate. 6. Repeat step 4 and step 5 for 500 iterations or epochs. For each iteration, calculate the cross-entropy loss on validation set. 7. Calculate the accuracy and macro-average precision, recall, and F1 score and provide the confusion matrix on the test set. 8. Experiment with varying values of = (0.0001, 0.001, 0.01, 0.1). Report your observations with respect to the performance of the model. You can vary the number of iterations to enhance the performance if necessary.