I'm a commerce student. I started watching your videos because of time series analysis of stock prices. Your data science are still so easy for me to understand.
I really love the way you summarize a lot of the concepts, this is helping me a lot to understand other lectures in some of my courses. Great Job! Many thanks!
Deeply appreciate the importance of choosing loss function for different models. As a beginner in data science it makes a lot of sense and better understanding of concepts.
Hi! Is there a way to choose a loss function from a predefined list or even to define a loss function by myself to use it with scikit-learn algorithms? Or do I have to write a whole ML algorithm from scratch to change default loss function?
Is it possible to apply a custom loss function in a regression model (or any other algorithm for predicting continuous variable) ? I'm working on a stock market prediction model and I need to maximize the following loss function: if [predicted] < [actual] then [predicted] else [-actual]. Would that be possible ? Thanks
Thank you. For the square loss, why don't we heard about cubic loss or quartic loss? Also is the square loss choice also based on its derivative compared to a linear loss?
Squares are used to avoid negative values. Also the square increases the range, penalizing the worse values. Cubic (or any odd power) will turn negatives. Using hight positive powers might hurt discovery and/or make analysis hard (the values will be too big or small depending on domain). It also gets expensive
@@pizzaguy8484 it isn't indeed haha, but i saw no comments and then commented, i'm a latin one, and i prefer english content because im learning tghe language, you comment really helped me , thank you so much
This is such an undervalued channel. You are out here making data scientists out of everyday people. Thank you for your content.
This brought it all together so nicely. You're a great teacher
Thank you so much!
I'm a commerce student. I started watching your videos because of time series analysis of stock prices.
Your data science are still so easy for me to understand.
I really love the way you summarize a lot of the concepts, this is helping me a lot to understand other lectures in some of my courses. Great Job! Many thanks!
Great to hear!
This is the best explanation of Loss function among all blogs, videos I came across
Deeply appreciate the importance of choosing loss function for different models. As a beginner in data science it makes a lot of sense and better understanding of concepts.
Exceptionally gifted with intelligence, and the ability to understand subjects and transfer knowledge. Thank you!
I learn a lot with your videos. Simple and straigh to the point. Thanks for contributing my dear!
I really like your videos! Just followed your channel last night and I am looking forward to more of your future videos!
Going through Data Science bootcamp at Flatiron School. This was supa' helpful. Thank you!
Complete new to ML and this was just amazing. Thank you!
Great to hear!
yoo this was exactly what I needed! You speak so clearly too thanks man
Glad I could help!
Wath an amazing tutorial you managed to give a detailed yet simple explanation, gained a subscriber
Very clear presentation. Thanks for keeping it simple.
absolutely amazing, your understanding and your explanation, both
Ritvikmath ... you are a great communicator... thanks
great video！！！awesome！
PERFECTLY explained! Thanks🙌
Glad it was helpful!
very helpful for my exam. thanks
Just a suggestion. a series on stochastic processes and possibly even short rate models.
Amazing Video
Another great video. Well done!
Thank you! Cheers!
Please explain what is an ACTIVE Function, you are great!!!!
Perfect! Thank you
really well explained great teacher!
Glad it was helpful!
Amazing explanation
Amazing video ! Nicely explained .
Thank you, really good explained. You helped me to understand my lecture
You're amazing! Keep up the great work!
Great explanation! Thank you so much!
thank you ... great explanation
Glad it was helpful!
nice examples!
Thanks, very helpful. Could you please do a video on Structural VARs (SVAR)? I'm really struggling with this concept. Greetings from Holland!
Amasing video, thank you!!!!
Hi! Is there a way to choose a loss function from a predefined list or even to define a loss function by myself to use it with scikit-learn algorithms? Or do I have to write a whole ML algorithm from scratch to change default loss function?
Amazing
thank you
incredible !!!! so good!!!!!
How would the minimum of weights to a hinge loss function be determined?
OMG this is so useful
thank u teacher
do i use 0-1 Loss if I am using a DecisionTreeClassifier?
Thanks
very nice lecture
thank you
we need The practical example with python
That catch in the beginning😂😂
Can the loss functions be applied to all model indiscriminately? For example, exponential loss function to Support vector classifier? Thanks.
In this video f(xi) is called the score
and exp (-yi f(xi)) is called the loss,
so what is y1 (f(xi) called? Does it have a name?
awesome vid!
Is it possible to apply a custom loss function in a regression model (or any other algorithm for predicting continuous variable) ? I'm working on a stock market prediction model and I need to maximize the following loss function: if [predicted] < [actual] then [predicted] else [-actual]. Would that be possible ? Thanks
Hi.. can you make one video on how a white noise effect model while NLP algorithms are used
Thanks
Thank you. For the square loss, why don't we heard about cubic loss or quartic loss? Also is the square loss choice also based on its derivative compared to a linear loss?
Squares are used to avoid negative values. Also the square increases the range, penalizing the worse values. Cubic (or any odd power) will turn negatives. Using hight positive powers might hurt discovery and/or make analysis hard (the values will be too big or small depending on domain). It also gets expensive
POV: youtuber puts in ten times the effort to explain a concept compared to your university professor
almost let the pen fall on this one hahahah
haha!
I'm starting in data sciense, i'm not bad at math, butg my question has always been "how do i convert non-number data into number-data?, im noob 😴
you should check one-hot encoding, but I don't think it's related to this video at all.
@@pizzaguy8484 it isn't indeed haha, but i saw no comments and then commented, i'm a latin one, and i prefer english content because im learning tghe language, you comment really helped me , thank you so much
@@javierargueta7355 No worries! Good luck with your studies, don't give up!
Data science is a very vast topic . Any suggestions how to master it