Machine Learning Crash Course: The Bias-Variance Dilemma
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Machine Learning Crash Course: The Bias-Variance Dilemma
1–10 of 63 posts
Re: Machine Learning Crash Course: The Bias-Variance Dilemma
#2wow nice.
Then I have things to do on my flight now.
Re: Machine Learning Crash Course: The Bias-Variance Dilemma
#3Brilliant post; Thank you;
Re: Machine Learning Crash Course: The Bias-Variance Dilemma
#4This seems to ultimately come down to an idea that folks have a hard time shaking. It is entirely possible that you cannot recover the original signal using machine learning. This is, fundamentally, what separates this field from digital sampling.
And this is not unique to machine learning, per se. https://fivethirtyeight.com/features/trump-noncitizen-voters... has a great widget that shows that as you get more data, you do not necessarily decrease inherent noise. In fact, it stays very constant. (Granted, this is in large because machine learning has most of its roots in statistics.)
More explicitly, with ML, you are building probabilistic models. This is contrasted to most models folks are used to which are analytic models. That is, you run the calculations for an object moving across the field, and you get something within the measurement bounds that you expected. With a probabilistic model, you get something that is within the bounds of being in line with previous data you have collected.
(None of this is to say this is a bad article. Just a bias to keep in mind as you are reading it. Hopefully, it helps you challenge it.)
Re: Machine Learning Crash Course: The Bias-Variance Dilemma
#5Wow, the discussion on the Fukushima civil engineering decision was pretty interesting. However, I find it surprising that the engineers simply overlooked the linearity of the law and used a nonlinear model. I wonder if there were any economic / other incentives at play, and the model shown was just used to justify the decision?
Regardless, that post was a great read.
Re: Machine Learning Crash Course: The Bias-Variance Dilemma
#6The whole problem of overfitting or underfitting exists because you're not trying to understand the underlying model, but you're trying to "cheat" by inventing some formula that happens to work in most cases.
Re: Machine Learning Crash Course: The Bias-Variance Dilemma
#7Wow, the discussion on the Fukushima civil engineering decision was pretty interesting. However, I find it surprising that the engineers simply overlooked the linearity of the law and used a nonlinear model. I wonder if there were any economic / other incentives at play, and the model shown was just used to justify the decision? Regardless, that post was a great read.
Most likely, since building a facility to survive a 2.5x stronger shake would surely be a lot more expensive.
I was also curious about how the data in the past few years did not follow the same trend as before. Does anyone know if that is what geologists call to be 'overdue' to an earthquake? Like California is supposed to be for a while?
Re: Machine Learning Crash Course: The Bias-Variance Dilemma
#8Here are parts 1, 2 & 3:
Introduction, Regression/Classification, Cost Functions, and Gradient Descent:
https://ml.berkeley.edu/blog/2016/11/06/tutorial-1/
Perceptrons, Logistic Regression, and SVMs:
https://ml.berkeley.edu/blog/2016/12/24/tutorial-2/
Neural networks & Backpropagation:
Re: Machine Learning Crash Course: The Bias-Variance Dilemma
#9Wow, the discussion on the Fukushima civil engineering decision was pretty interesting. However, I find it surprising that the engineers simply overlooked the linearity of the law and used a nonlinear model. I wonder if there were any economic / other incentives at play, and the model shown was just used to justify the decision? Regardless, that post was a great read.
Most likely, since building a facility to survive a 2.5x stronger shake would surely be a lot more expensive. I was also curious about how the data in the past few years did not follow the same trend as before. Does anyone know if that is what geologists call to be 'overdue' to an earthquake? Like California is supposed to be for a while?
Well, the data wasn't showing that the past few years were anomalous; rather, there were fewer high-magnitude earthquakes than expected. I don't think this has anything to do with being overdue for an earthquake. Most likely this is just because with events of low frequency (e.g. these higher-magnitude earthquakes were predicted to occur once every ~100 years by the linear model), large percent deviations from the expected value are more probable. Basically if you flip a coin 10 times you might imagine that 3 heads and 7 tails is pretty common, whereas 300 heads and 700 tails on 1000 tosses is comparitively extremely unlikely.
Re: Machine Learning Crash Course: The Bias-Variance Dilemma
#10The whole problem of overfitting or underfitting exists because you're not trying to understand the underlying model, but you're trying to "cheat" by inventing some formula that happens to work in most cases.
May I ask how you reached this insight? What field do you work in?