代写Data science Assignment代写Processing

日期：2025-02-20 06:15

Data science

Assignment

Due: 5pm EST, 2/21/2025

1.  n-gram

Given the training data

<s>  John  read  a  book  by  Jane  </s>

<s>  John  read  another  book  </s>

<s>  I  read  a  different  book  </s>

(a)  Calculate bigrams using maximum likelihood estimates (MLE) and fill out the table.

Bigram

Probability

Bigram

Probability

P(John | <s>)		P(another | read)
P(read | John)		P(book | another)
P(a | read)		P(</s> | book)
P(book | a)		P(I | <s>)
P(by | book)		P(read | I)
P(Jane | by)		P(different | a)
P(<s> | Jane)		P(book | different)

(b)  Calculate the sentence probability of <s>  John  read  a  different  book  </s> using only MLE bigram.

(c)  Calculate the sentence probability of <s>  Jane  read  a  book  </s> using only MLE bigram.

2. Evaluation metrics on binary classification

Given the following output,

Actual Label	Predicted Label
0	0
1	1
0	1
0	1
1	1
0	0
1	1
0	1
1	0
0	1

(a)  Draw the confusion matrix.

(b)  Calculate the Accuracy, Precision, Recall, and F1 score.

(c)  Why might using accuracy as the only metric is not ideal?

3. Evaluation metrics on multiclass classification

Given the following confusion matrix of a multi-label classifier

Truth

	A	B	C	D	E	F
A	95	1	13	0	1	0
B	0	1	0	0	0	0
C	10	90	0	1	0	0
D	0	0	0	34	3	7
E	0	1	2	13	26	5
F	0	0	2	14	5	10

Classifier

(a)  Calculate the precision, recall, and F1 for classes A-F

(b)  Calculate the micro-average precision, recall, and F1

(c)  Calculate the macro-average precision, recall, and F1

4.  Text classfication

The drug review dataset provides patient reviews on drugs and a positive and negative rating reflecting overall patient satisfaction.  The dataset consists of two files:  drug   review train .csv for training and drug   review test .csv for testing.  Both files contain plain-text, UTF8-encoded sample set in a tab-separated format with the following columns:

•  Text

•  Binary label (0 and 1)

(a)  Use BernoulliNB to build a naıve Bayes classifier(¨).

BernoulliNB	true positive	false positive	false negative	precision	recall	F1-score
positive
negative

(b)  Repeat the process in Task (a), but use the SVM (SGDClassifier) model.

SGDClassifier	true positive	false positive	false negative	precision	recall	F1-score
positive
negative

(c)  Upload the source codes.