What I’m doing is simply converting the table into its JSON form, and then using this to create a table using the multi-row VALUES syntax which paradoxically allows expressions. The expression I’m using is JSON_Value, which allows me do effectively dictate the source within the table, via that JSON Path expression, and the destination. As it is an expression, I can do all sorts of manipulation as well as a transpose. I could, if I wanted, (in SQL 2017)provide that path parameter as a variable. This sort of technique can be used for several other reporting purposes, and it is well-worth experimenting with it because it is so versatile.
That is not at all what I would have thought up; very interesting approach. I’d probably just be lazy and shell out to R Services.
I was sitting in a bagel shop on Saturday with my 9 year old daughter. We had brought along hexagonal graph paper and a six sided die. We decided that we would choose a hexagon in the middle of the page and then roll the die to determine a direction:
1 up (North)
2 diagonal to the upper right (Northeast)
3 diagonal to the lower right (Southeast)
4 down (South)
5 diagonal to the lower left (Southwest)
6 diagonal to the upper left (Northwest)
Our first roll was a six so we drew a line to the hexagon northwest of where we started. That was the first “step.”
After a few rolls we found ourselves coming back along a path we had gone down before. We decided to draw a second line close to the first in those cases.
We did this about 50 times. The results are pictured above, along with kid hands for scale.
Last Monday we celebrated a “Scientific Marathon” at Royal Botanic Garden in Madrid, a kind of mini-conference to talk about our research. I was talking about the relation between fungal spore size and environmental variables such as temperature and precipitation. To make my presentation more friendly, I created a GIF to explain the Brownian Motion model. In evolutionary biology, we can use this model to simulate the random variation of a continuous trait through time. Under this model, we can notice how closer species tend to maintain closer trait values due to shared evolutionary history. You have a lot of information about Brownian Motion models in evolutionary biology everywhere!
Another place that this is useful is in describing stock market movements in the short run.
As you can see, I collected the brand (Peugeot 106), the type (1.0, 1.1, …), the color of the car (black, blue, …) the construction year of the car, the odometer of the car (which is the distance in kilometers (km) traveled with the car at this point in space and time), the ask price of the car (in Euro’s), the days until the MOT (Ministry of Transport test, a required periodical check-up of your car) and the horse power (HP) of the car. Feel free to use your own variables/units!
It’s an interesting example of how you can approach a real problem.
Now, we implement a similar representation of the transient change outlier by taking advantage of the arimax() function within the TSA package. The arimax() function requires to specify some ARMA parameters, and that is done by capturing the seasonality as discussed in ref. . Further, the transient change is specified by means of xtransf and transfer input parameters. The xtransf parameter is a matrix with each column containing a covariate that affects the time series response in terms of an ARMA filter of order (p,q). For our scenario, it provides a value equal to 1 at the outliers time index and zero at others. The transfer parameter is a list consisting of the ARMA orders for each transfer covariate. For our scenario, we specify an AR order equal to 1.
Check it out.
The first thing to notice is that our values are not normalized. The number of visitors is a number and gets larger and larger. To normalize it, we simply divide it by 100, since all numbers are below 1. The same holds for the lag. Most of the lags are lower than 30. Therefore, I will divide the lag size by 30.
Notice that there are many more approaches for normalizing the data! This is just a quick normalization on the data, but feel free to use your own normalization method. My normalization process is closely related to the MinMaxScalar normalization which can be found in sklearn (scikit-learn).
With just a few lines of Python code we can create a Multi-Layer Perceptron (MLP):
Click through for the code.
Pro Tip: A quick test is to see if the log transformation increases the magnitude of the correlation between “TotalCharges” and “Churn”. We’ll use a few
dplyroperations along with the
corrrpackage to perform a quick correlation.
correlate(): Performs tidy correlations on numeric data
focus(): Similar to
select(). Takes columns and focuses on only the rows/columns of importance.
fashion(): Makes the formatting aesthetically easier to read.
This is a very useful tutorial.
There are a few things worth mentioning. Often, Pandas is abbreviated as pd (like Numpy which is often abbreviated as np). If you look at other code, you will see that DataFrames are often abbreviated by df. Here, the DataFrame is constructed using data from a list of lists. The columns argument specifies the keys of the data.
This is a high-level intro, but helps you get your feet wet if you’ve not played with the library.
Kathryn Bryant and M. Aaron Owen share their Kaggle experiences. First, Kathryn, et al:
The lifecycle of our project was a typical one. We started with data cleaning and basic exploratory data analysis, then proceeded to feature engineering, individual model training, and ensembling/stacking. Of course, the process in practice was not quite so linear and the results of our individual models alerted us to areas in data cleaning and feature engineering that needed improvement. We used root mean squared error (RMSE) of log Sale Price to evaluate model fit as this was the metric used by Kaggle to evaluate submitted models.
Data cleaning, EDA, feature engineering, and private train/test splitting (and one spline model!) were all done in R but we used Python for individual model training and ensembling/stacking. Using R and Python in these ways worked well, but the decision to split work in this manner was driven more by timing than anything else.
Some variables had a moderate amount of missingness. For example, about 17% of the houses were missing the continuous variable, Lot Frontage, the linear feet of street connected to the property. Intuitively, attributes related to the size of a house are likely important factors regarding the price of the house. Therefore, dropping these variables seems ill-advised.
Our solution was based on the assumption that houses in the same neighborhood likely have similar features. Thus, we imputed the missing Lot Frontage values based on the median Lot Frontage for the neighborhood in which the house with missing value was located.
This is the major upside to Kaggle: it gives you the ability to work in a controlled environment with real data sets, which include real data problems. Yeah, the data’s much cleaner than you’d experience in production pretty much anywhere, but that lets you practice technique with a relatively low barrier to entry. H/T R-Bloggers (Kathryn | Aaron)
Ladies and gentlemens, this is one of the most perfect IDEs for editing your Python code! At least in my opinion. Jupyter notebook is a web based code editor and can quickly generate visualizations. You can mix up code and text containing no, simple or complex mathematics. One thing I am missing here, is the support for code completion, but there are tons of plugins available so this should be no problem. It is also easy to turn your notebook into a presentation. For collaboration with non-technical teams, this is a great tool.
Conclusion: perfect Python IDE for data science! Less support for code inspection.
Click through for reviews of three IDEs.
The primary problem is that these classes are imbalanced: the red points are greatly outnumbered by the blue.
Research on imbalanced classes often considers imbalanced to mean a minority class of 10% to 20%. In reality, datasets can get far more imbalanced than this. Here are some examples:
- About 2% of credit card accounts are defrauded per year. (Most fraud detection domains are heavily imbalanced.)
- Medical screening for a condition is usually performed on a large population of people without the condition, to detect a small minority with it (e.g., HIV prevalence in the USA is ~0.4%).
- Disk drive failures are approximately ~1% per year.
- The conversion rates of online ads has been estimated to lie between 10-3 to 10-6.
- Factory production defect rates typically run about 0.1%.
Many of these domains are imbalanced because they are what I call needle in a haystackproblems, where machine learning classifiers are used to sort through huge populations of negative (uninteresting) cases to find the small number of positive (interesting, alarm-worthy) cases.
Read on for some good advice on how to handle imbalanced data.