1. Candidate Elimination Algorithm | Solved Example - 1 | Machine Learning by Mahesh Huddar
Вставка
- Опубліковано 19 вер 2020
- Candidate Elimination Algorithm Solved Example - 1 Machine Learning by Mahesh Huddar
Candidate Elimination Algorithm Solved Example - 1:
Web: www.vtupulse.com/machine-lear...
Video: • 1. Candidate Eliminati...
Candidate Elimination Algorithm Solved Example - 2:
Web: www.vtupulse.com/machine-lear...
Video: • 2. Candidate Eliminati...
Candidate Elimination Algorithm Solved Example - 3:
Web: www.vtupulse.com/machine-lear...
Video: • 3. Candidate Eliminati...
4. Candidate Elimination Algorithm Solved Example: • 4 Candidate Eliminatio...
Machine Learning - • Machine Learning
Big Data Analysis - • Big Data Analytics
Data Science and Machine Learning - Machine Learning - • Machine Learning
Python Tutorial - • Python Application Pro...
candidate elimination algorithm,
candidate elimination algorithm example,
candidate elimination algorithm machine learning,
candidate elimination algorithm python,
candidate elimination Solved,
candidate elimination solved example,
candidate elimination VTU,
candidate elimination VTU ML,
candidate elimination VTU Lab,
VTU ML Lab,
candidate elimination algorithm Implementation,
code wrestling,
candidate elimination algorithm youtube
Thank you sir, my teacher did this in one class and he doesn't even teach the complete concept.
They never do
My teacher never even took its name
Same brother 😂
watching this day before the exam. Very well explained. Keep uploading more videos.
Thank you sir for making this so easy and understandable.
Thank you sir because of you, my teacher can learn from you and can teach us....like ditto
Welcome
Do like share and subscribe
Very well explained!
Thank you Sir,Really a nice video,crystal clears the concept.
you are a life saver
Tq sir... understandable at once..
Beautifully explained 👍
Tq so much for the video. It is really useful. We expect more videos like this..😊
Thank You
Do search like
candidate elimination algorithm Mahesh Huddar
You will get all my videos related to candidate elimination algorithm
Do like share and subscribe
Explanation is damn good 🔥
Very Good Explanation Sir
sir,how to calculate numerically no of consistent hypothesis.
5:30
Why you don't check the all attribute??
Sir how to decide whether the geberal hypothesis is inconsistent???
Fantastic sir 🙏🙏🙏🙏
Well understood
Well explained
sir at g3 isnt same producing inconsistance with the last last column of the last instance??
Yes it is
no you have do that till 3rd instance only
Can you explain why this?@@HG-xz7jy
Thank you so much sir! Fabulous explanation
Thank you sir👍
Thanks sir!
Thank you..sir
so how we can use it to classify new items
Excellent 👌
nice video on candidate ellimination
explaination is good ..but lot of confusion
Tqsm sir.....
Sir what If attribute values have more than 2 possible values.
Could you please write a example of selection sort code
Bro eer itthnda tension ye aapuii🤣🤣❤️❤️❤️
sir plzzzz provide for Big data analytics imp ques link also 2018 scheme
Did not understand the last part...
ThanQ sir..
@@MaheshHuddar kits,jntu university
Thanks a ton sir!!!
Most welcome!
Do like share and subscribe
Thank you Darling ❤
Good explanation 🎉
Welcome
Do like share and subscribe
Very helpful ❤
Thank You
Please do like share and subscribe
Thank s for this clear and easy to grab explanation.
Welcome
Do like share and subscribe
Thank you sir....!
Welcome
Do like share and subscribe
Thank you sir Done exam😊😊🎉🎉❤
Welcome
Do like share and subscribe
at 10:56, 'same' does not match with 'change' of the last example, and the classification is negative, so they are inconsistent. why didn't you remove (?,?,?,?,?,Same) ?
comparison is done from the beginning to the current row. The current row is 3rd so that is the last row that the consistency needs to be satisfied till.
amazing explanation
Welcome
Do like share and subscribe
Thank you so much
Welcome
Do like share and subscribe
sir provide imp ques for AI &ML 2018 scheme 7th sem
best !!
superr sir
What if Attributes 1 have three possible value , in this case what I should write opposite of Rainy
Consider both options
Amazing Sir
Thank You
Do like share and subscribe
@@MaheshHuddar sure sir
Thanks.
Welcome
Do like share and subscribe
in G3 you are writing ???? cool ?...... since 4th training example is not known to the model when we are handling g3... so how exactly cool opposite word will come to know at this point of time
Thank You Sir
Welcome
Do like share and subscribe
Sir for S3 we should also check for other element aslo right or should we check the only first elemnt in S? In that case what you said is wrong sir
Sir I have exam at 1 pm today Plz reply me sir ???
Sir,is is ok even one attribute value satisfys the hypothesis?
@@MaheshHuddar thanku sir 💐
can you please solve this dataset:
1 Sunny Warm Normal Strong Warm Same Yes
2 Sunny Warm High Strong Warm Same Yes
3 Rainy Cold High Strong Warm Change No
4 Sunny Warm High Strong Cool Change Yes
Sir for example 3 it's given 'No' but in s2 wind and water attributes are matching. So it is inconsistent can we replace with ' ? 'Mark or not?.
For Positive examples (Yes) -
If all attributes match then the example is consistent, if any one of the attributes does not match then inconsistent
For Negative examples (No) -
If all attributes match then the example is inconsistent, if any one of the attributes does not match then consistent
Please watch one more time you will understand
@@MaheshHuddar❤thank you for clarifying
Welcome
Do like share and subscribe
Tq
Welcome
Do like share and subscribe
version space could be in any order sir????
super sir
Thank You
Do like share and subscribe
Sir what does ? represent and phy represent in a hypothesis?
? means any value in acceptable
Phy means nothing is acceptable
1match show super
@@MaheshHuddar I said it is superb explanation
👏
Do Like Share and Subscribe
How does the set of hypotheses look like after having handled the following three instances:
Instance 1. { (Angry Black Horse) (Happy Brown Cow) } positive
Instance 2. { (Angry Brown Cow) (Happy Black Horse)} positive
Instance 3. { (Angry Brown Horse) (Happy Brown Horse) } negative
Using Depth-First Search :
CBH3 ={ (Angry ? ?) (Happy ? ?) }
CBH3={ (Angry ? Horse) (Happy ? Cow) }
CBH3=(Angry Black ?) (Happy Brown ?)}
Yes, the answer is correct.
Score: 1
Accepted Answers:
CBH3=(Angry Black ?) (Happy Brown ?)}
kindly explain
why not this is correct option CBH3 ={ (Angry ? ?) (Happy ? ?) }
well
🐐 goat