"Is it bad to get the answer a different way? Will they mark that as not knowing Bayes Theorem or just correct as it is an easier way to get the answer?
The way I went is to look at what happens when the factory makes 100 light bulbs. Machine A makes 60 of which 3 are faulty, Machine B makes 40 of which 1.2 are faulty. Therefore the pool of faulty lightbulbs is 3/4.2 = 5/7 from machine A and 1.2/4.2 = 3/7 from Machine B."
Will I. - "Is it bad to get the answer a different way? Will they mark that as not knowing Bayes Theorem or just correct as it is an easier way to get the answer?
The way I went is to look at what happens when the factory makes 100 light bulbs. Machine A makes 60 of which 3 are faulty, Machine B makes 40 of which 1.2 are faulty. Therefore the pool of faulty lightbulbs is 3/4.2 = 5/7 from machine A and 1.2/4.2 = 3/7 from Machine B."See full answer
"1) select avg(session) from table where session> 180
2) select round(sessiontime/300)*300 as sessionbin, count() as sessioncount from table group by round(sessiontime/300)300 order by session_bin
3) SELECT t1.country AS country_a,
t2.country AS country_b
FROM (
SELECT country, COUNT(*) AS session_count
FROM yourtablename
GROUP BY country
) AS t1
JOIN (
SELECT country, COUNT(*) AS session_count
FROM yourtablename
`GROUP BY countr"
Erjan G. - "1) select avg(session) from table where session> 180
2) select round(sessiontime/300)*300 as sessionbin, count() as sessioncount from table group by round(sessiontime/300)300 order by session_bin
3) SELECT t1.country AS country_a,
t2.country AS country_b
FROM (
SELECT country, COUNT(*) AS session_count
FROM yourtablename
GROUP BY country
) AS t1
JOIN (
SELECT country, COUNT(*) AS session_count
FROM yourtablename
`GROUP BY countr"See full answer
"Over-fitting of a model occurs when model fails to generalize to any new data and has high variance withing training data whereas in under fitting model isn't able to uncover the underlying pattern in the training data and high bias.
Tree based model like decision tree and random forest are likely to overfit whereas linear models like linear regression and logistic regression tends to under fit.
There are many reasons why a Random forest can overfits easily 1. Model has grown to its full depth a"
Jyoti V. - "Over-fitting of a model occurs when model fails to generalize to any new data and has high variance withing training data whereas in under fitting model isn't able to uncover the underlying pattern in the training data and high bias.
Tree based model like decision tree and random forest are likely to overfit whereas linear models like linear regression and logistic regression tends to under fit.
There are many reasons why a Random forest can overfits easily 1. Model has grown to its full depth a"See full answer
"BETWEEN and HAVING clauses in SQL serve different purposes:
1. BETWEEN Clause
Used to filter rows based on a range of values.
Works with numeric, date, or text values.
Can be used with WHERE or HAVING clauses.
The range includes both lower and upper bounds.
Example: Filtering employees with salaries between 30,000 and 50,000
`SELECT * FROM Employees
WHERE salary BETWEEN 30000 AND 50000;`
2. HAVING Clause
Used to filter **groups"
Meenakshi D. - "BETWEEN and HAVING clauses in SQL serve different purposes:
1. BETWEEN Clause
Used to filter rows based on a range of values.
Works with numeric, date, or text values.
Can be used with WHERE or HAVING clauses.
The range includes both lower and upper bounds.
Example: Filtering employees with salaries between 30,000 and 50,000
`SELECT * FROM Employees
WHERE salary BETWEEN 30000 AND 50000;`
2. HAVING Clause
Used to filter **groups"See full answer
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"Reinforcement Learning is a type of machine learning where an agent learns to make decisions by trying out different actions and receiving rewards or penalties in return. The goal is to learn, over time, which actions yield the highest rewards.
There are three core components in RL:
The agent — the learner or decision-maker (e.g., an algorithm or robot),
The environment — everything the agent interacts with,
Actions and rewards — the agent takes actions, and the environmen"
Constantin P. - "Reinforcement Learning is a type of machine learning where an agent learns to make decisions by trying out different actions and receiving rewards or penalties in return. The goal is to learn, over time, which actions yield the highest rewards.
There are three core components in RL:
The agent — the learner or decision-maker (e.g., an algorithm or robot),
The environment — everything the agent interacts with,
Actions and rewards — the agent takes actions, and the environmen"See full answer
"I can try to summarize their discussion as I remembered.
Linear regression is one of the method to predict target (Y) using features (X).
Formula for linear regression is a linear function of features. The aim is to choose coefficients (Teta) of the prediction function in such a way that the difference between target and prediction is least in average.
This difference between target and prediction is called loss function. The form of this loss function could be dependent from the particular real"
Ilnur I. - "I can try to summarize their discussion as I remembered.
Linear regression is one of the method to predict target (Y) using features (X).
Formula for linear regression is a linear function of features. The aim is to choose coefficients (Teta) of the prediction function in such a way that the difference between target and prediction is least in average.
This difference between target and prediction is called loss function. The form of this loss function could be dependent from the particular real"See full answer
"Deep Learning is a part of Artificial Intelligence, it's like teaching the machine to think and make decisions on its own. It's like how we teach a child the concept of an apple - it's round, red, has a stem on top. We show them multiple pictures of apples and then they understand and can recognize an apple in future. Similarly, we feed lots of data to the machine, and slowly, it starts learning from that data, and can then make relevant predictions or decisions based on what it has learnt.
A co"
Surbhi G. - "Deep Learning is a part of Artificial Intelligence, it's like teaching the machine to think and make decisions on its own. It's like how we teach a child the concept of an apple - it's round, red, has a stem on top. We show them multiple pictures of apples and then they understand and can recognize an apple in future. Similarly, we feed lots of data to the machine, and slowly, it starts learning from that data, and can then make relevant predictions or decisions based on what it has learnt.
A co"See full answer
"SQL databases are relational, NoSQL databases are non-relational. SQL databases use structured query language and have a predefined schema. NoSQL databases have dynamic schemas for unstructured data. SQL databases are vertically scalable, while NoSQL databases are horizontally scalable."
Ali H. - "SQL databases are relational, NoSQL databases are non-relational. SQL databases use structured query language and have a predefined schema. NoSQL databases have dynamic schemas for unstructured data. SQL databases are vertically scalable, while NoSQL databases are horizontally scalable."See full answer
"Clarification questions
What is the purpose of connecting the DB?
Do we expect high-volumes of traffic to hit the DB
Do we have scalability or reliability concerns?
Format
Code -> DB
Code -> Cache -> DB
API -> Cache -> DB - APIs are built for a purpose and have a specified protocol (GET, POST, DELETE) to speak to the DB. APIs can also use a contract to retrieve information from a DB much faster than code.
Load balanced APIs -> Cache -> DB
**Aut"
Aaron W. - "Clarification questions
What is the purpose of connecting the DB?
Do we expect high-volumes of traffic to hit the DB
Do we have scalability or reliability concerns?
Format
Code -> DB
Code -> Cache -> DB
API -> Cache -> DB - APIs are built for a purpose and have a specified protocol (GET, POST, DELETE) to speak to the DB. APIs can also use a contract to retrieve information from a DB much faster than code.
Load balanced APIs -> Cache -> DB
**Aut"See full answer
"Number of employees after the first year = n*(1+r) = n1
Number of employees after the second year = n1(1+r) = n(1+r)**2
Hence, the number of employees after 't' years = n(1+r)*t"
Asish B. - "Number of employees after the first year = n*(1+r) = n1
Number of employees after the second year = n1(1+r) = n(1+r)**2
Hence, the number of employees after 't' years = n(1+r)*t"See full answer