In this post I’ll share how I’ve added a LetsEncrypt SSL certificate to a subdomain at VPS with Centos 7 using Vesta CP.
Author: Igor Savinkin
This post is devoted to the steps of how to create subdomain (Centos 7 and Vesta CP) and map a [Laravel] project folder to it.
Recently we encountered a new powerful scraping service called Web Scraper IDE [of Bright Data]. The life-test and thorough drill-in are coming soon. Yet now we want to highlight its main features that has badly (in positive sense, strongly) impressed us.
- Remove previous git origin
git remote remove origin
- Add new origin with PAT (<Token>) :
git remote add origin https://<TOKEN>@github.com/<USERNAME>/<REPO>.git
- Push once with –set-upstream
git push --set-upstream origin main
Now you might commit changes to the remote repo without adding PAT into a push command every time.
If you need to create PAT, use the following tut.
Categories
Random Forest vs Gradient boosting
The objective of the task is to build a model so that we can, as optimally as this data allows, relate molecular information, to an actual biological response.
We have shared the data in the comma separated values (CSV) format. Each row in this data set represents a molecule. The first column contains experimental data describing an actual biological response; the molecule was seen to elicit this response (1), or not (0). The remaining columns represent molecular descriptors (D1 through D1776), these are calculated properties that can capture some of the characteristics of the molecule – for example size, shape, or elemental constitution. The descriptor matrix has been normalized.
Categories
Bagging and Random Forest
In this post we do several tasks performing the Bagging and the Random Forest Classificators.
We gradually develop classifier for the Bagging on randomized trees that in its final stage matches the Random Forest algorithm.
We’ll also build the RandomForestClassifier of sklearn.ensemble and learn of its quality depending on (1) number of trees, (2) max features used for each tree node, and (3) max tree depth.
Bagging is an ensemble algorithm that fits multiple models on different subsets of a training dataset, then combines the predictions from all models.
Random forest is an extension of bagging that also randomly selects subsets of features used in each data sample.
Categories
Sklearn, Random Forest
The objective of the task is to build a model so that we can, as optimally as this data allows, relate molecular information, to an actual biological response.
We have shared the data in the comma separated values (CSV) format. Each row in this data set represents a molecule. The first column contains experimental data describing an actual biological response; the molecule was seen to elicit this response (1), or not (0). The remaining columns represent molecular descriptors (D1 through D1776), these are calculated properties that can capture some of the characteristics of the molecule – for example size, shape, or elemental constitution. The descriptor matrix has been normalized.
Categories
Sklearn Decision trees
Suppose there is a table like below (1 info row only):
| Blows Minute (BPM) |
Speed (RPM) | Power, PSI | Flow, PSI | Tool Sys |
|---|---|---|---|---|
| 0-2500 | 0-250 | 1.8 HP | 2.6-13.2 GPM | SDS Max |
How to scrape it using cheerio.js as a parser?
Case 1 (1 row only)
The Bike Sharing Demand problem requires using historical data on weather conditions and bicycle rental to predict the number of occupied bicycles (rentals) for a certain hour of a certain day.
In the original problem statement, there are 11 features available. The feature set contains both real, categorical, and binary data. For the demonstration, a training sample bike_sharing_demand.csv is used from the original data.
See the Bike Sharing Demand, part 1 of the task where we performed some initial problem analysis.