Often under social network research computing k-cores gives you a similar pruning effect. But since our network is quite big I have used a poor man's version of that pruning approach: We will be going through a while loop that throws out columns in our case users where the sum of their edges less than 50 and rows pages where the sum of their edges less than This is equivalent to throwing out users that follow less than 50 pages and throwing out pages that have less than users.
Since a removal of a page or user might hurt one of our conditions again, we will continue reduce the network as long as still columns or rows need to be removed. Once both conditions are met the loop will stop. While pruning we are also updating our user and pages lists via boolean filtering , so we can track which users like which pages and vice versa.
This process quite significantly reduces our network size to roughly a couple thousand users 19k with a couple of thousands pages k or attributes each. What I will not show here is my second failed attempt at predicting the psychological traits. While the results were slightly better due to the better signal to noise ratio, they were still quite unsatisfactory.
That's where our second very classic trick comes into play: dimensionality reduction. In our case these variables are pages that a user liked.
So there might be a page called "Britney Spears" and another one "Britney Spears Fans" and all users that like the first also like the second. Intuitively we would want both pages to "behave" like one, so kind of merge them into one page.
For such approaches a number of methods are available - although they all work a little bit different - the most used examples are Principal component analysis , Singular value decomposition , Linear discriminant analysis. And as a benefit these dimensions are sorted in the way that the most important ones come first. Each bucket will contain pages that are similar in regard to how users perceive these pages. Finally we can correlate these factors with the users personality traits.
An even more popular approach often used in recommender systems is SVD, which is fast, easy to compute and yields good results. In the case above I have reduced the thousands of pages into just 5 features for visualization purposes. We can now do a pairwise comparison between the personality traits and the 5 factors that we computed and visualize it in a heatmap. In the heatmap above we see that factor 3 seems to be quite highly positively correlated with the user's openness. We also see that factor 1 is negatively correlated with age. So the older you get the less you probably visit pages from this area.
Generally we see though that the correlations between some factors and traits are not very high though e.
Armed with our new features we can come back and try to build a model that finally will do what I promised: namely predict the user's traits based solely on those factors. What I am not showing here, is the experimentation of choosing the right model for the job.
After trying out a few models like Linear Regression , Lasso or decision trees the LassoLars Model with cross validation worked quite well. I also applied some poormans hyperparameter tuning, where all the predictions are going through different variants of k of the SVD dimensionality reduction. To see which amount of dimensions gave us the best results we can simply look at the printout or visualize it nicely with seaborn below. In the case above I found that solutions with 90 dimensions gave me quite good results. A more production ready way of doing is can be done with GridSearch , but I wanted to keep the amount of code for this example minimal.
So now we can finally look at how the model performed on each trait, when using 90 dimensions in the SVD.
Among all traits openness seems to be the most predictable attribute. It seems to make sense, as open people would be willing to share more of their likes on facebook. Hotel Chocolat Chocolate Shop. Pet Drugs Online Pet Store. Clintons Company. KidStart Website. Information about Page Insights Data.
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To assist with start up, the software was written to initially accept an Anytone D code plug. This movement resulted in MASI to increase by 4. Read our cookies policy. How this site works We think it's important you understand the strengths and limitations of the site. Loan money. MSE welcomes applications from all students who possess a love for innovation and aspire to be at the forefront of technology.
Recommendations and Reviews. See More. October I trust Martin Lewis I think his advice is trustworthy and a sincere desire to help people he has September February 19, Green Slime. Spooky Fruity. The result is the analyst-in-the-loop approach that makes the best use of human and machine automated tasks.
Prophet begins by modeling a time series using the analysts specified parameters, producing forecasts and then evaluating them. When a problem occurs or poor performance is detected, Prophet surfaces these issues to the analyst to help them understand what went wrong and how to adjust the model based on the feedback.
Prophet automatically evaluates forecast performance and flags issues that warrant manual intervention. One of the easiest evaluation methods is to set a baseline with some simple forecasting methods e. It is useful to compare simplistic and advanced forecasting methods to determine whether additional performance can be gained by using a more complex model.
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Sometimes, it may be better to just use a simplistic model! The other method Prophet evaluates performance is by using a procedure called simulated historical forecasts SHFs. SHFs works by producing K forecasts at various time points within the history, which is then fit to a model of the expected error at different forecast horizons.
For a more detailed description of how Prophet works, check out the paper here. Start by importing all the necessary libraries. Reference this guide if you need more help. For this example, we will use the avocado price dataset from the Hass Avocado Board.
The dataset contains information for avocado sales starting in up until May From late to mid, prices were relatively stable before sliding to an all-time low during summer However, prices recovered remarkably and peaked in November , before again dropping to a near all-time low in early Prices rebounded to an all-time high in September and has plummeted since. Next, plot the forecast by calling plot and passing in the forecast dataframe. The black dots represent outliers and the light-blue shaded regions are the uncertainty intervals.