fast_ml fast_ml-2014 fast_ml-2014-56 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: RStudio is an IDE for R. It gives the language a bit of a slickness factor it so badly needs. The nice thing about the software, beside good looks, is that it integrates console, help pages, plots and editor (if you want it) in one place. For example, instead of switching for help to a web browser, you have it right next to the console. The same thing with plots: in place of the usual overlapping windows, all plots go to one pane where you can navigate back and forth between them with arrows. You can save a plot as an image or as a PDF. While saving as image you are presented with a preview and you get to choose the image format and size. That’s the kind of detail that shows how RStudio makes working with R better. You can have up to four panes on the screen: console source / data plots / help / files history and workspace Arrange them in any way you like. The source pane has a run button, so you can execute the current line of code in the console, or selec
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1 It gives the language a bit of a slickness factor it so badly needs. [sent-2, score-0.202]
2 The nice thing about the software, beside good looks, is that it integrates console, help pages, plots and editor (if you want it) in one place. [sent-3, score-0.715]
3 For example, instead of switching for help to a web browser, you have it right next to the console. [sent-4, score-0.328]
4 The same thing with plots: in place of the usual overlapping windows, all plots go to one pane where you can navigate back and forth between them with arrows. [sent-5, score-0.844]
5 While saving as image you are presented with a preview and you get to choose the image format and size. [sent-7, score-0.438]
6 That’s the kind of detail that shows how RStudio makes working with R better. [sent-8, score-0.124]
7 You can have up to four panes on the screen: console source / data plots / help / files history and workspace Arrange them in any way you like. [sent-9, score-1.507]
8 The source pane has a run button, so you can execute the current line of code in the console, or select a few lines and execute the whole region. [sent-10, score-1.098]
9 There’s a R script for each unit and Trevor walks you through it in a video. [sent-12, score-0.06]
10 Instead of typing each command (which has its advantages) or copy-and-pasting, you just click once. [sent-13, score-0.072]
11 The workspace shows data, variable values and functions. [sent-14, score-0.272]
12 The history pane is somewhat underpowered - for example one can’t select a few lines (or even just one) and copy them to clipboard. [sent-16, score-0.867]
13 One can send them to the console and to the source pane, though. [sent-17, score-0.607]
14 RStudio also has a few nice color schemes, particularly with dark backgrounds. [sent-18, score-0.371]
15 One shortcoming is that the dark ones don’t mix that well with white background for history, help and plots. [sent-19, score-0.557]
16 The software is available for Linux, Mac and Windows. [sent-20, score-0.059]
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same-blog 1 1.0 56 fast ml-2014-03-31-If you use R, you may want RStudio
Introduction: RStudio is an IDE for R. It gives the language a bit of a slickness factor it so badly needs. The nice thing about the software, beside good looks, is that it integrates console, help pages, plots and editor (if you want it) in one place. For example, instead of switching for help to a web browser, you have it right next to the console. The same thing with plots: in place of the usual overlapping windows, all plots go to one pane where you can navigate back and forth between them with arrows. You can save a plot as an image or as a PDF. While saving as image you are presented with a preview and you get to choose the image format and size. That’s the kind of detail that shows how RStudio makes working with R better. You can have up to four panes on the screen: console source / data plots / help / files history and workspace Arrange them in any way you like. The source pane has a run button, so you can execute the current line of code in the console, or selec
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Introduction: When it comes to machine learning, most software seems to be in either Python, Matlab or R. Plus native apps, that is, compiled C/C++. These are fastest. Most of them is written for Unix environments, for example Linux or MacOS. So how do you run them on your computer if you have Windows installed? Back in the day, you re-partitioned your hard drive and installed Linux alongside Windows. The added thrill was, if something went wrong, your computer wouldn’t boot. Now it’s easier. You just run Linux inside Windows, using what’s called a virtual machine. You need virtualization software and a machine image to do so. Most popular software seems to be VMware. There is also VirtualBox - it is able to run VMware images. We have experience with WMware mostly, so this is what we’ll refer to. VMware player is free to download and use. There are also many images available, of various flavours of Linux and other operating systems. In fact, you can run Windows inside Linux if you wish
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Introduction: IPython is known for the notebooks. But the first thing they list on their homepage is a “powerful interactive shell”. And that’s true - if you use Python interactively, you’ll dig IPython. Here are a few features that make a difference for us: command line history and autocomplete These are basic shell features you come to expect and rely upon after a first contact with a Unix shell. The standard Python interpreter feels somewhat impoverished without them. %paste Normally when you paste indented code, it won’t work. You can fix it with this magic command. other %magic commands Actually, you can skip the % : paste , run some_script.py , pwd , cd some_dir shell commands with ! For example !ls -las . Alas, aliases from your system shell don’t work. looks good Red and green add a nice touch, especially in a window with dark background. If you have a light background, use %colors LightBG Share your favourite features
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3 0.089845784 21 fast ml-2013-02-27-Dimensionality reduction for sparse binary data
Introduction: Much of data in machine learning is sparse, that is mostly zeros, and often binary. The phenomenon may result from converting categorical variables to one-hot vectors, and from converting text to bag-of-words representation. If each feature is binary - either zero or one - then it holds exactly one bit of information. Surely we could somehow compress such data to fewer real numbers. To do this, we turn to topic models, an area of research with roots in natural language processing. In NLP, a training set is called a corpus, and each document is like a row in the set. A document might be three pages of text, or just a few words, as in a tweet. The idea of topic modelling is that you can group words in your corpus into relatively few topics and represent each document as a mixture of these topics. It’s attractive because you can interpret the model by looking at words that form the topics. Sometimes they seem meaningful, sometimes not. A meaningful topic might be, for example: “cric
4 0.089018978 51 fast ml-2014-01-25-Why IPy: reasons for using IPython interactively
Introduction: IPython is known for the notebooks. But the first thing they list on their homepage is a “powerful interactive shell”. And that’s true - if you use Python interactively, you’ll dig IPython. Here are a few features that make a difference for us: command line history and autocomplete These are basic shell features you come to expect and rely upon after a first contact with a Unix shell. The standard Python interpreter feels somewhat impoverished without them. %paste Normally when you paste indented code, it won’t work. You can fix it with this magic command. other %magic commands Actually, you can skip the % : paste , run some_script.py , pwd , cd some_dir shell commands with ! For example !ls -las . Alas, aliases from your system shell don’t work. looks good Red and green add a nice touch, especially in a window with dark background. If you have a light background, use %colors LightBG Share your favourite features
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Introduction: Perhaps the most common format of data for machine learning is text files. Often data is too large to fit in memory; this is sometimes referred to as big data. But do you need to load the whole data into memory? Maybe you could at least pre-process it line by line. We show how to do this with Python. Prepare to read and possibly write some code. The most common format for text files is probably CSV. For sparse data, libsvm format is popular. Both can be processed using csv module in Python. import csv i_f = open( input_file, 'r' ) reader = csv.reader( i_f ) For libsvm you just set the delimiter to space: reader = csv.reader( i_f, delimiter = ' ' ) Then you go over the file contents. Each line is a list of strings: for line in reader: # do something with the line, for example: label = float( line[0] ) # .... writer.writerow( line ) If you need to do a second pass, you just rewind the input file: i_f.seek( 0 ) for line in re
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Introduction: This time we enter the Stack Overflow challenge , which is about predicting a status of a given question on SO. There are five possible statuses, so it’s a multi-class classification problem. We would prefer a tool able to perform multiclass classification by itself. It can be done by hand by constructing five datasets, each with binary labels (one class against all others), and then combining predictions, but it might be a bit tricky to get right - we tried. Fortunately, nice people at Yahoo, excuse us, Microsoft, recently relased a new version of Vowpal Wabbit , and this new version supports multiclass classification. In case you’re wondering, Vowpal Wabbit is a fast linear learner. We like the “fast” part and “linear” is OK for dealing with lots of words, as in this contest. In any case, with more than three million data points it wouldn’t be that easy to train a kernel SVM, a neural net or what have you. VW, being a well-polished tool, has a few very convenient features.
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Introduction: There’s a contest at Kaggle held by Qatar University. They want to be able to discriminate men from women based on handwriting. For a thousand bucks, well, why not? Congratulations! You have spotted the ceiling cat. As Sashi noticed on the forums , it’s not difficult to improve on the benchmarks a little bit. In particular, he mentioned feature selection, normalizing the data and using a regularized linear model. Here’s our version of the story. Let’s start with normalizing. There’s a nice function for that in R, scale() . The dataset is small, 1128 examples, so we can go ahead and use R. Turns out that in its raw form, the data won’t scale. That’s because apparently there are some columns with zeros only. That makes it difficult to divide. Fortunately, we know just the right tool for the task. We learned about it on Kaggle forums too. It’s a function in caret package called nearZeroVar() . It will give you indexes of all the columns which have near zero var
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