Tom Lous builds a solution to link similar companies together by address:

Recently a colleague asked me to help her with a data problem, that seemed very straightforward at a glance.

She had purchased a small set of data from the chamber of commerce (Kamer van Koophandel: KvK) that contained roughly 50k small sized companies (5–20FTE), which can be hard to find online.

She noticed that many of those companies share the same address, which makes sense, because a lot of those companies tend to cluster in business complexes.

Read on for the solution. Like many data problems, it turns out to be a lot more complicated than you’d think at first glance.

Maria Jesus Alonso explains decision trees and their subsequent improvements:

Bagging (or Bootsrap Aggregating), the second prediction technique brought to the BigML Dashboard and API, uses a collection of trees (rather than a single one), each tree built with a different random subset of the original dataset for each model in the ensemble. Specifically, BigML defaults to a sampling rate of 100% (with replacement) for each model. This means some of the original instances will be repeated and others will be left out. Bagging performs well when a dataset has many noisy features and only one or two are relevant. In those cases, Bagging will be the best option.

Random Decision Forests extend the Bagging technique by only considering a random subset of the input fields at each split of the tree. By adding randomness in this process, Random Decision Forests help avoid overfitting. When there are many useful fields in your dataset, Random Decision Forests are a strong choice.

Click through for how boosted trees change this model a bit.

Now we have a form that

`lm`

can work with. We just need to specify a set of inputs that are powers of`x`

(as in a traditional polynomial fit), and a set of inputs that are`y`

times powers of`x`

. This may seem like a strange thing to do, because we are making a model where we would need to know the value of`y`

in order to predict`y`

. But the trick here is that we will not try to use the fitted model to predict anything; we will just take the coefficients out and rearrange them in a function. The`fit_pade`

function below takes a dataframe with`x`

and`y`

values, fits an`lm`

model, and returns a function of`x`

that uses the coefficents from the model to predict`y`

:

The lm function does more than just fit straight lines.

Mala Mahadevan explains the Central Limit Theorem with an example:

The

central limit theoremstates that the sampling distribution of the mean of any independent,random variable will be normal or nearly normal, if the sample size is large enough. How large is “large enough”? The answer depends on two factors.

- Requirements for accuracy. The more closely the sampling distribution needs to resemble a normal distribution, the more sample points will be required.
- The shape of the underlying population. The more closely the original population resembles a normal distribution, the fewer sample points will be required. (from stattrek.com).
The main use of the sampling distribution is to verify the accuracy of many statistics and population they were based upon.

Read on for an example and to see how to calculate this in T-SQL.

Ananda Das explains how linear Support Vector Machines work in classifying spam messages:

Linear SVM assumes that the two classes are linearly separable that is a hyper-plane can separate out the two classes and the data points from the two classes do not get mixed up. Of course this is not an ideal assumption and how we will discuss it later how linear SVM works out the case of non-linear separability. But for a reader with some experience here I pose a question which is like this Linear SVM creates a discriminant function but so does LDA. Yet, both are different classifiers. Why ? (Hint: LDA is based on Bayes Theorem while Linear SVM is based on the concept of margin. In case of LDA, one has to make an assumption on the distribution of the data per class. For a newbie, please ignore the question. We will discuss this point in details in some other post.)

This is a pretty math-heavy post, so get your coffee first. h/t R-Bloggers.

Mala Mahadevan covers some basics of probability:

Probability is an important statistical and mathematical concept to understand. In simple terms – probability refers to the chances of possible outcome of an event occurring within the domain of multiple outcomes. Probability is indicated by a whole number – with 0 meaning that the outcome has no chance of occurring and 1 meaning that the outcome is certain to happen. So it is mathematically represented as P(event) = (# of outcomes in event / total # of outcomes). In addition to understanding this simple thing, we will also look at a basic example of conditional probability and independent events.

It’s a good intro to a critical topic in statistics. If I would add one thing to this, it would be to state that probability is **always** conditional upon something. It’s fair to write something as P(Event) understanding that it’s a shortcut, but in reality, it’s always P(Event | Conditions), where Conditions is the set of assumptions we made in collecting this sample.

Alex Smolyanskaya explains some common errors when doing time series analysis:

Non-zero model error indicates that our model is missing explanatory features. In practice, we don’t expect to get rid of all model error—there will be some error in the forecast from unavoidable natural variation. Natural variation should reflect all the stuff we will probably never capture with our model, like measurement error, unpredictable external market forces, and so on. The distribution of error should be close to normal and, ideally, have a small mean. We get evidence that an important explanatory variable is missing from the model when we find that the model error doesn’t look like simple natural variation—if the distribution of errors skews one way or another, there are more outliers than expected, or if the mean is unpleasantly large. When this happens we should try to identify and correct any missing or incorrect model features.

It’s an interesting article, especially the bit about cross-validation, which is a perfectly acceptable technique in non-time series models.

Project structures often organically grow to suit people’s needs, leading to different project structures within a team. You can consider yourself lucky if at some point in time you find, or someone in your team finds, a obscure blog post with a somewhat sane structure and enforces it in your team.

Many years ago I stumbled upon ProjectTemplate for R. Since then I’ve tried to get people to use a good project structure. More recently DrivenData (what’s in a name?) released their more generic Cookiecutter Data Science.

The main philosophies of those projects are:

A consistent and well-organized structure allows people to collaborate more easily.

Your analyses should be reproducible and your structure should enable that.

A projects starts from raw data that should never be edited; consider raw data immutable and only edit derived sources.

This is a set of prescriptions and focuses on the phase before the project actually kicks off.

Mala Mahadevan shows how to generate a frequency table in T-SQL and in R:

My results are as below. I have 1000 records in the table. This tells me that I have 82 occurences of age cohort 0-5, 8.2% of my dataset is from this bracket, 82 again is the cumulative frequency since this is the first record and 8.2 cumulative percent. For the next bracket 06-12 I have 175 occurences, 17.5 %, 257 occurences of age below 12, and 25.7 % of my data is in this age bracket. And so on.

Click through for the T-SQL and R scripts.

Rodrigo Agundez looks at Prophet, Facebook’s new API for store sales forecasting:

The data is of a current client, therefore I won’t be disclosing any details of it.

Our models make forecasts for different shops of this company. In particular I took 2 shops, one which contains the easiest transactions to predict from all shops, and another with a somewhat more complicated history.

The data consists of real transactions since 2014. Data is daily with the target being the number of transactions executed during a day. There are missing dates in the data when the shop closed, for example New Year’s day and Christmas.

The holidays provided to the API are the same I use in our model. They contain from school vacations or large periods, to single holidays like Christmas Eve. In total, the data contains 46 different holidays.

It looks like Prophet has some limitations but can already make some nice predictions.

Kevin Feasel

2017-04-10

Data Science, Spark