SQL Joins

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Joins are used to combine rows from two or more tables based on related columns between them. Joins allow you to retrieve data from multiple tables simultaneously, enabling you to create complex queries that fetch data from different sources. There are different types of joins in SQL, including: INNER JOIN Returns only the rows that have matching values in both tables based on the specified join condition. It discards non-matching rows from both tables. Example:           create table t1(x int); insert into t1 values(1); insert into t1 values(1); insert into t1 values(0); create table t2(y int); insert into t2 values(0); insert into t2 values(1); insert into t2 values(1);           select * from t1 inner join t2 on t1.x = t2.y Output: 2. LEFT JOIN (or) LEFT OUTER JOIN Returns all the rows from the left (or first) table and the matching rows from the right (or second) table. If there is no match, NULL values are
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Machine Learning Foundation - Summary of Regression - Quiz Answers

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1. The simple threshold classifier for sentiment analysis described in the video (check all that apply):

a) Must have pre-defined positive and negative attributes
b) Must either count attributes equally or pre-define weights on attributes
c) Defines a possibly non-linear decision boundary

2. For a linear classifier classifying between “positive” and “negative” sentiment in a review x, Score(x) = 0 implies (check all that apply):

a) The review is very clearly “negative”
b) We are uncertain whether the review is “positive” or “negative”
c) We need to retrain our classifier because an error has occurred

3. For which of the following datasets would a linear classifier perform perfectly?

b

4. True or false: High classification accuracy always indicates a good classifier.

a) True
b) False

5.  True or false: For a classifier classifying between 5 classes, there always exists a classifier with accuracy greater than 0.18.

a) True
b) False

6. True or false: A false negative is always worse than a false positive.

a) True
b) False

7. Which of the following statements are true? (Check all that apply)

a) Test error tends to decrease with more training data until a point, and then does not change (i.e., curve flattens out)
b) Test error always goes to 0 with an unboundedly large training dataset
c) Test error is never a function of the amount of training data

Comments

  1. UnknownFebruary 20, 2018 at 3:07 AM

    All bad answers. Learn properly

    ReplyDelete
  2. UnknownAugust 25, 2018 at 2:08 AM

    Answered for question no 1 is both a and b, rest answers are correct

    ReplyDelete

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