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R Neural Network example
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R Neural Network example

  #11 (permalink)
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SMCJB View Post
Yes i've found the whole concept of random forests very very interesting and had a lot of fun investigating/optimizing with leaf sizes and mtry settings. Specific to the Kaggle Titanic problem though I've found that my models are overfitting the data and out of sample scores are lower, which wasnt as much the case when i was doing Logistic regression (before I knew about Forests). I have thought of some potential trading applications but it'll probably be months before I start investigating them. I don't have anything to base this on, but I was expecting SVM and NN to be better for trading.

Thanks Sody I'll definitely take a look. I like watching video's like that during the trading day when things get quiet. I assume if you buy the course you can go back and rewatch the video's as much as you like?

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ML: things to be cautious about

Just be careful when using random forests or any decision tree method. Like NN approaches, over-fitting the training data is quite easy to achieve. RF models tend to minimize this somewhat by spreading the error across many "trees" in the forest. But this doesn't mean it's most accurate.

Also, in your machine learning quest, you're most likely to come across ARMA/ARIMA as well as GARCH models for time series data. While these do a pretty good job in non-financial time series applications, random effects models tend to our-perform these times series models when we're trying to model ts data that have alot of "noise" inside them (aka, any equity price series). We can convert the price series to (log) returns, but this (surprisingly) doesn't benefit the ARIMA/GARCH models alot.

You'll be able to build JUST as powerful of a model if you focus on simple linear regression models (at first) and spend time understanding "what drives your predictions". Sometimes we call this "feature selection" or "feature engineering". You'll likely find that you get more milage out of providing a decently capable model with excellent predictive input rather than trying to create a more complex model.

Just a few notes from the field.

Best of luck,
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Thanks Dwayne,

dpaschall View Post
Just be careful when using random forests or any decision tree method. Like NN approaches, over-fitting the training data is quite easy to achieve. RF models tend to minimize this somewhat by spreading the error across many "trees" in the forest. But this doesn't mean it's most accurate.

I've actually found that increasing the minimum leaf size, while decreasing the accuracy of the in sample prediction, actually increases the accuracy in the out of sample data (reduces over-fitting I suspect). At least in the Kaggle/Titanic case.


dpaschall View Post
Also, in your machine learning quest, you're most likely to come across ARMA/ARIMA as well as GARCH models for time series data. While these do a pretty good job in non-financial time series applications, random effects models tend to our-perform these times series models when we're trying to model ts data that have alot of "noise" inside them (aka, any equity price series). We can convert the price series to (log) returns, but this (surprisingly) doesn't benefit the ARIMA/GARCH models alot.

I'm familiar with Garch Volatility models, but not with theory behind them. Thanks for the heads up.


dpaschall View Post
You'll be able to build JUST as powerful of a model if you focus on simple linear regression models (at first) and spend time understanding "what drives your predictions". Sometimes we call this "feature selection" or "feature engineering". You'll likely find that you get more milage out of providing a decently capable model with excellent predictive input rather than trying to create a more complex model.

One of the reasons I started the Kaggle challenge was to get some real world experience in feature engineering, something I've enjoyed and learnt from. Your comments regarding linear models and understanding what is driving the predictions are interesting. In the coursera.org course Andrew Ng talked about making additional features by combining and/or transforming current features. I have tried this a couple of times, and not surprisingly the more features I add, the more accuarte the prediction becomes. The problem is this seems to reek of over fitting. I can see how at times a log transformation etc can help, but I don't see how generalizing 'y ~ a + b +c' to 'y ~ a^3 + a^2b +a^2c + ab^2 + ac^2 + abc +... etc' makes sound logical sense.

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I think some of this would be better discussed in a more appropriate thread such as

https://futures.io/elite-circle/23861-machine-learning-ai-discussion-generic.html

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