Tsuyoshi Matsuzaki demonstrates the process in a post at the MSDN Blog. The post explores the Multi-Domain Sentiment Dataset, a collection of product reviews from Amazon.com. The dataset includes reviews from 975,194 products on Amazon.com from a variety of domains, and for each product there is a text review and a star rating of 1, 2, 4, or 5. (There are no 3-star rated reviews in the data set.) Here’s one example, selected at random:
What a useful reference! I bought this book hoping to brush up on my French after a few years of absence, and found it to be indispensable. It’s great for quickly looking up grammatical rules and structures as well as vocabulary-building using the helpful vocabulary lists throughout the book. My personal favorite feature of this text is Part V, Idiomatic Usage. This section contains extensive lists of idioms, grouped by their root nouns or verbs. Memorizing one or two of these a day will do wonders for your confidence in French. This book is highly recommended either as a standalone text, or, preferably, as a supplement to a more traditional textbook. In either case, it will serve you well in your continuing education in the French language.
The review contains many positive terms (“useful”, “indespensable”, “highly recommended”), and in fact is associated with a 5-star rating for this book. The goal of the blog post was to find the terms most associated with positive (or negative) reviews. One way to do this is to use the
featurizeTextfunction in thje Microsoft ML package included with Microsoft R Client and Microsoft R Server. Among other things, this function can be used to extract ngrams (sequences of one, two, or more words) from arbitrary text. In this example, we extract all of the one and two-word sequences represented at least 500 times in the reviews. Then, to assess which have the most impact on ratings, we use their presence or absence as predictors in a linear model:
If you’re thinking about sentiment analysis, read the whole thing.
So that looks much better — clean, short, and easy to understand. But is it fast? Rather than grabbing the first lines of each group, it has to go searching for duplicates. But avoiding grouping and ungrouping must save some time.
So I ran some
Click through for techniques and timings. I’m not surprised that the “classic” method won out in terms of time, but for explanatory value, I’d definitely prefer trying to explain the tidyverse distinct version. H/T R-Bloggers
We use the app in question to compare search interest for R data Science versus Python Data Science, see above chart. It looks like until December 2016, R dominated, but fell below Python by early 2017. The above chart displays an interest index, 100 being maximum and 0 being minimum. Click here to access this interactive chart on Google, and check the results for countries other than US, or even for specific regions such as California or New York.
Note that Python always dominated R by a long shot, because it is a general-purpose language, while R is a specialized language. But here, we compare R and Python in the niche context of data science. The map below shows interest for Python (general purpose) per region, using the same Google index in question.
It’s an interesting look at the relative shift between R and Python as a primary language for statistical analysis.
In this tutorial you’ll learn how to:
- Read text into R
- Select only certain lines
- Tokenize text using the tidytext package
- Calculate token frequency (how often each token shows up in the dataset)
- Write reusable functions to do all of the above and make your work reproducible
For this tutorial we’ll be using a corpus of transcribed speech from bilingual children speaking in English. You can find more information on this dataset and download it here.
It’s a nice tutorial, especially because the data set is a bit of a mess.
From the above results, it is observed that the F-statistic value is 17.94 and it is highly significant as the corresponding p-value is much less than the level of significance (1% or 0.01). Thus, it is wise to reject the null hypothesis of equal mean value of mileage run across all the tyre brands. In other words, the average mileage of the four tyre brands are not equal.
Now you have to find out the pair of brands which differ. For this you may use the Tukey’s HSD test.
ANOVA is a fairly simple test, but it can be quite useful to know.
Last year, this column, let’s call it
spam, had values
bad. This year the column is called
Spamand the values are
2. When I found out that this was the source of the problem, I just had to change the arguments of my functions from
generate_spam_plot(dataset = data2016, column = spam, value = 1) generate_spam_plot(dataset = data2016, column = spam, value = 0)
generate_spam_plot(dataset = data2017, column = Spam, value = 1) generate_spam_plot(dataset = data2017, column = Spam, value = 2)
without needing to change anything else. This is why I use
tidyeval; without it, writing a function such as
genereta_spam_plotwould not be easy. It would be possible, but not easy.
Read the whole thing.
In Figure 15, I set the filter to be
tcp.srcport==50755, and then I applied the filter by clicking the arrow. To start using this:
- Clear the Process Monitor display, and make sure you are capturing events.
- Start WireShark capturing (Ctrl+E). If you get a question whether you want to save the captured packets, just click “Continue without Saving”.
- Execute the code in Code Snippet 3.
The Process Monitor output looks almost the same as in Figure 9, whereas the WireShark output looks like so:
Niels also includes a recap to help people who haven’t been following along get up to speed.
An important thing to remember in boosting is that the base learner which is being boosted should not be a complex and complicated learner which has high variance for e.g a neural network with lots of nodes and high weight values.For such learners boosting will have inverse effects.
So I will explain Boosting with respect to decision trees in this tutorial because they can be regarded as weak learners most of the times.We will generate a gradient boosting model.
Click through for more details. H/T R-Bloggers
Classification and regression tree (or decision tree) is broadly used machine learning method for modeling. They are favorite because of these factors:
- simple to understand (white box)
- from a tree we can extract interpretable results and make simple decisions
- they are helpful for exploratory analysis as binary structure of tree is simple to visualize
- very good prediction accuracy performance
- very fast
- they can be simply tuned by ensemble learning techniques
But! There is always some “but”, they poorly adapt when new unexpected situations (values) appears. In other words, they can not detect and adapt to change or concept drift well (absolutely not). This is due to the fact that tree creates during learning just simple rules based on training data. Simple decision tree does not compute any regression coefficients like linear regression, so trend modeling is not possible. You would ask now, so why we are talking about time series forecasting with regression tree together, right? I will explain how to deal with it in more detail further in this post.
This was a very interesting article. Absolutely worth reading. H/T R-Bloggers
When browsing for the symbols, you can use this command:
x /1 *!TCP*. By using the option
/1you’ll only see the names, and no addresses. On my machine that gives me quite a lot, but there are two entries that catch my eye:
sqllang!Tcp::Close. So let us set breakpoints at those two symbols, and see what happens when we execute our code.
The result when executing the code is that we initially break at
sqllang!Tcp::AcceptConnection. Followed somewhat later by breaking at
sqllang!Tcp::Close. Cool, this seems to work – let us set some more breakpoints and try to figure out the flow of events.
The first half recapitulates his previous findings, and then he incorporates new information in the second half.