Reddit Reddit reviews The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

We found 20 Reddit comments about The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). Here are the top ones, ranked by their Reddit score.

Artificial Intelligence & Semantics
AI & Machine Learning
Computer Science
Computers & Technology
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
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20 Reddit comments about The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics):

u/fusionquant · 46 pointsr/algotrading

First of all, thanks for sharing. Code & idea implementation sucks, but it might turn into a very interesting discussion! By admitting that your trade idea is far from being unique and brilliant, you make a super important step in learning. This forum needs more posts like that, and I encourage people to provide feedback!

Idea itself is decent, but your code does not implement it:

  • You want to holds stocks that are going up, right? Well, imagine a stock above 100ma, 50ma, 20ma, but below 20ma and 10ma. It is just starting to turn down. According to your code, this stock is labeled as a 'rising stock', which is wrong.

  • SMAs are generally not cool. Not cool due to lag of 1/2 of MA period.

  • Think of other ways to implement your idea of gauging "going up stocks". Try to define what is a "stock that is going up".

  • Overbought/oversold part. This part is worse. You heard that "RSI measures overbought/oversold", so you plug it in. You have to define "Overbought/oversold" first, then check if RSI implements your idea of overbought/oversold best, then include it.

  • Since you did not define "overbought / oversold", and check whether RSI is good for it, you decided to throw a couple more indicators on top, just to be sure =) That is a bad idea. Mindlessly introducing more indicators does not improve your strategy, but it does greatly increase overfit.

  • Labeling "Sell / Neutral / Buy " part. It is getting worse =)) How did you decide what thresholds to use for the labels? Why does ma_count and oscCount with a threshold of 0 is the best way to label? You are losing your initial idea!
    Just because 0 looks good, you decide that 0 is the best threshold. You have to do a research here. You'd be surprised by how counter intuitive the result might be, or how super unstable it might be=))

  • Last but not least. Pls count the number of parameters. MAs, RSI, OSC, BBand + thresholds for RSI, OSC + Label thresholds ... I don't want to count, but I am sure it is well above 10 (maybe 15+?). Now even if you test at least 6-7 combinations of your parameters, your parameter space will be 10k+ of possible combinations. And that is just for a simple strategy.

  • With 10k+ combinations on a daily data, I can overfit to a perfect straight line pnl. There is no way with so many degrees of freedom to tell if you overfit or not. Even on a 1min data!

    The lesson is: idea first. Define it well. Then try to pick minimal number of indicators (or functions) that implement it. Check for parameter space. If you have too many parameters, discard your idea, since you will not be able to tell if it is making/losing money because it has an edge or just purely by chance!

    What is left out of this discussion: cross validation and picking best parameters going forward

    Recommended reading:
u/zorfbee · 32 pointsr/artificial

Reading some books would be a good idea.

u/ultraliks · 16 pointsr/datascience

Sounds like you're looking for the statistical proofs behind all the hand waving commonly done by "machine learning" MOOCS. I recommend this book. It's very math heavy, but it covers the underlying theory well.

u/krunk7 · 10 pointsr/programming


Check out The Elements of Statistical Learning and Introduction to Machine Learning.

edit those books are about practical applications of what we've learning to date from the neural network style of pattern classification. So it's not about modeling an actual biological neuron. For modeling of the biology, it's been a while since I futzed with that. But when I wrote a paper on modeling synaptic firing, Polymer Solutions: An Introduction to Physical Properties was the book for that class. Damned if I remember if that book has the details I needed or if I had to use auxiliary materials though.

u/effernand · 5 pointsr/learnmachinelearning

When I started on the field I took the famous course on Coursera by Andrew Ng. It helped to grasp the major concepts in (classical) ML, though it really lacked on mathematical profundity (truth be told, it was not really meant for that).

That said, I took a course on edX, which covered things in a little more depth. As I was getting deeper into the theory, things became more clear. I have also read some books, such as,

  • Neural Networks, by Simon Haikin,
  • Elements of Statistical Learning, by Hastie, Tibshirani and Friedman
  • Pattern Recognition and Machine Learning, by Bishop

    All these books have their own approach to Machine Learning, and particularly I think it is important that you have a good understanding on Machine Learning, and its impacts on various fields (signal processing, for instance) before jumping into Deep Learning. Before almost three years of major dedication in studying the field, I feel like I can walk a little by myself.

    Now, as a begginer in Deep Learning, things are a little bit different. I would like to make a few points:

  • If you have a good base on maths and Machine Learning, the algorithms used in Deep Learning will be more straightforward, as some of them are simply an extension of previous attempts.
  • The practical part in Machine Learning seems a little bit childish with respect to Deep Learning. When I programmed Machine Learning models, I usually had small datasets, and algorithms who could run in a simple CPU.
  • As you begin to work with Deep Learning, you will need to master a framework of your choice, which will yield issues about data usage (most datasets do not fit into memory), GPU/memory management. For instance, if you don't handle your data well, it becomes a bottleneck that slows down your code. So, when compared with simple numpy + matplotlib applications, tensorflow API's + tensorboard visualizations can be tough.

    So, to summarize, you need to start with simple, boring things until you can be an independent user of ML methods. THEN you can think about state-of-the-art problems to solve with cutting-edge frameworks and APIs.
u/awesome_hats · 4 pointsr/datascience

Well I'd recommend:

u/bailey_jameson · 3 pointsr/MachineLearning
u/kylebalkissoon · 3 pointsr/algotrading

You're a savage, reading sheets of dead trees with ink squirted upon them...

Be careful about the editions as you need to make sure its the jan 2013 print to be up to date.

u/hell_0n_wheel · 3 pointsr/Cloud

Machine learning isn't a cloud thing. You can do it on your own laptop, then work your way up to a desktop with a GPU, before needing to farm out your infrastructure.

If you're serious about machine learning, you're going to need to start by making sure your multivariate calculus and linear algebra is strong, as well as multivariate statistics (incl. Bayes' theorem). Machine learning is a graduate-level computer science topic, because it has these heady prerequisites.

Once you have these prereqs covered, you're ready to get started. Grab a book or online course (see links below) and learn about basic methods such as linear regression, decision trees, or K-nearest neighbor. And once you understand how it works, implement it in your favorite language. This is a great way to learn exactly what ML is about, how it works, how to tweak it to fit your use case.

There's plenty of data sets available online for free, grab one that interests you, and try to use it to make some predictions. In my class, we did the "Netflix Prize" challenge, using 100MM Netflix ratings of 20K different movies to try and predict what people like to watch. Was lots of fun coming up with an algorithm that wrote its own movie: it picked the stars, the genre and we even added on a Markov chain title generator.

Another way to learn is to grab a whitepaper on a machine learning method and implement it yourself, though that's probably best to do after you've covered all of the above.



Note: this coursera is a bit light on statistical methods, you might want to beef up with a book like this one.

Hope this helps!

u/thecity2 · 3 pointsr/MachineLearning

I would recommend Elements of Statistical Learning (the "ESL" book) for someone with your level of knowledge (they have an easier Intro book "ISL", but seems you could probably head straight for this):

u/alk509 · 2 pointsr/programming

I really liked the Witten & Frank book (we used it in my intro to machine learning class a few years ago.) It's probably showing its age now, though - they're due for a new edition...

I'm pretty sure The Elements of Statistical Learning is available as a PDF somewhere (check /r/csbooks.) You may find it a little too high-level, but it's a classic and just got revised last year, I think.

Also, playing around with WEKA is always fun and illuminating.

u/antounes · 2 pointsr/learnmachinelearning

I would mention Bishop's Pattern Recognition and Machine Learning ( as well as Hastie's Elements of Statistical Learning (

Sure they're not that easy to delve into, but they'll give you a very strong mathematical point of view,

good luck !

u/namnnumbr · 2 pointsr/datascience

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

u/throwawaystickies · 1 pointr/WGU

Thank you!! If you don't mind my asking, if you're working a full-time job, how much time have you been allocating for the program, and in how many months are you projected to finish?

Also, do you have any tips on how I can best prepare before entering the program? I'm considering reading the Elements of Statistics during commute instead of the usual ones I read and brush up on my linear algebra to prepare.