Reddit reviews Applied Predictive Modeling

There is the second edition (2018) of APM
Check out here:
https://www.amazon.com/Applied-Predictive-Modeling-Max-Kuhn/dp/1461468485

Applied Predictive Modeling by Kuhn and Johnson

Gives good interpretations of different approaches as well as listing the strengths, weaknesses, and ways to mitigate the weaknesses of those approaches. If you're an R user, this book is an excellent reference.

Quick and dirty answer: speaking very broadly, random forests -- found in the "randomForest" package -- tend to win battle-of-the-algorithms type studies. If you just want to play with a single model, I'd recommend starting with that and looking at the help for it.

Longer and better answer: Your best bet to answering all these questions and getting a good handle on data mining/predictive analytics is this book: Applied Predictive Modeling. The book references the "caret" package quite a bit, since the package's author is the same person. With it, you can train a lot of different types of models for regression or classification optimizing for accuracy, RMSE, ROC, etc. It provides a standard API for playing with models and makes your life much, much easier. It has its own website here.

Many thanks. I can speak more on the topic, but you're wanting to learn a lot about Machine learning (well lasso and ridge regression technically count as statistics, but point stands).

If you learn best via online courses, I'd suggest starting with Andrew Ng's Machine Learning Course

If you learn best through reading, I'd recommend two books: Hastie, Tibshirani, & Friedman - Elements of Statistical Learning
and Kuhn & Johnson - Applied Predictive Modeling

Obviously, I'd also recommend my blog once I learn my audience.

You're welcome. Thanks for providing some additional detail. This helps. I think if you read up on the CRISP-DM and use that framework to walk your way through some of these challenges it will be very beneficial to you. I'd recommend giving this document a read when you have the time. I think that if you show them that you are comfortable with these guidelines and know how to work your way through it to solve a problem it will go a long way. Model selection can be a bit tricky depending on the situation but I think most practitioners have a favorite model that they go to. Sounds like you're already familiar with Wolpert's "No Free Lunch Theorem" suggesting to try a wide variety of techniques. Personally, this is where I'd start digging deeper into tuning parameters (cross-validation, etc.) to help with that decision. Ultimately though, it's important to have a firm understanding of the strengths/weaknesses of the different models and their use cases so you can make an informed selection decision. Kuhn and Johnson's book Applied Predictive Modeling will be a good read to help you prepare.

It sounds like you're looking for time series material, but Applied Predictive Modeling may be of interest to you. For time series and R specifically, this text seems well-reviewed.

This book is all you will need.

https://www.amazon.com/Applied-Predictive-Modeling-Max-Kuhn/dp/1461468485/ref=nodl_

I ran across this presentation last night by Max Kuhn, one of the authors of Applied Predictive Modeling (http://www.amazon.com/Applied-Predictive-Modeling-Max-Kuhn/dp/1461468485).

https://static.squarespace.com/static/51156277e4b0b8b2ffe11c00/t/513e0b97e4b0df53689513be/1363020695985/KuhnENAR.pdf

It's a really great discussion of how they did the joint authoring of the book and the tools they used - and what they would do differently.

I read "Applied Predictive Modeling" by Kuhn. It has R-Labs at the end of each chapter with data. Very useful. I also read the R for Data Science and found this a good complement to it.

APM gives you enough of the theory behind the models to get a good understanding.

There's also the book "Intro To Statistical Learning" that is sort of like an abridged version of APM. Focuses just on the methods and R-Labs. Doesn't get too much into the theory and keeps it as a black box.

So if you want a deeper dive and have some time, I suggest APM, if you are on a time crunch, I'd suggest ISL


I think they're also available partially free on github. But I prefer having the book for quick reference.

Also check out Applied Predictive Modeling - it's in a way the next book to read after ISLR - it goes a bit more in depth about good practices, plusses and minuses of different models, feature creation/extraction.

Apparently I have to go to the Amazon page to find a table of contents(?)

EDIT: ok they added a TOC.

You'll probably find this article and its references interesting: https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining

I also strongly recommend this book: http://www.amazon.com/Guerrilla-Analytics-Practical-Approach-Working/dp/0128002182

If you're looking something more technical about actually doing analyses, this is book is very accessible: http://www.amazon.com/Applied-Predictive-Modeling-Max-Kuhn/dp/1461468485

If you use R, this book is really great: http://www.dcc.fc.up.pt/~ltorgo/DataMiningWithR/

I didn't know about MLR until this post. So without having spent any time with it whatsoever, I would only say that one of the nice things about the caret package is that you can also leverage Kuhn and Johnson's book, Applied Predictive Modeling, as well as YouTube videos of Max Kuhn discussing caret.