Scikit-learn Scraper
Spider read scikit-learn.org in 1.1 s without a browser and returned 673 lines of clean markdown.
**> . For example, scale each> attribute on the input vector X to [0,1] or [-1,+1], or standardize itto have mean 0 and variance 1. Note that the*> scaling must beapplied to the test vector to obtain meaningful results. See section> Preprocessing data> for more details on scaling and normalization.`](generated/sklearn.svm.NuSVC.html#sklearn.svm.NuSVC)> /`](generated/sklearn.svm.OneClassSVM.html#sklearn.svm.OneClassSVM)> /`](generated/sklearn.svm.NuSVR.html#sklearn.svm.NuSVR)> approximates the fraction of training errors and support vectors.`](generated/sklearn.svm.SVC.html#sklearn.svm.SVC)> , if data for classification are unbalanced (e.g. manypositive and few negative), set`> and/or trydifferent penalty parameters* **> Randomness of the underlying implementations**> : The underlyingimplementations of`](generated/sklearn.svm.SVC.html#sklearn.svm.SVC)> and`](generated/sklearn.svm.NuSVC.html#sklearn.svm.NuSVC)> use a random numbergenerator only to shuffle the data for probability estimation (when`> ). This randomness can be controlledwith the`> these estimators are not random and`> has no effect on theresults. The underlying`](generated/sklearn.svm.OneClassSVM.html#sklearn.svm.OneClassSVM)> implementation is similar tothe ones of`](generated/sklearn.svm.NuSVC.html#sklearn.svm.NuSVC)> . As no probability estimationis provided for`](generated/sklearn.svm.OneClassSVM.html#sklearn.svm.OneClassSVM)> , it is not random.`](generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC)> implementation uses a random number> generator to select features when fitting the model with a dual coordinatedescent (i.e when`> ). It is thus not uncommon,> to have slightly different results for the same input data. If that> happens, try with a smaller tol parameter. This randomness can also becontrolled with the`> the underlying implementation ofThe same call, in code.
The capture above came back as markdown. These examples add a key, so you get browser rendering, proxies, and concurrency on scikit-learn.org.
import { SpiderBrowser } from "spider-browser";
const spider = new SpiderBrowser({
apiKey: process.env.SPIDER_API_KEY!,
});
await spider.connect();
const page = spider.page!;
await page.goto("https://scikit-learn.org");
// No selectors, no schema. Spider reads the page and names the fields.
const data = await page.scrape();
console.log(data);
await spider.close();import { Spider } from "@spider-cloud/spider-client";
const spider = new Spider({ apiKey: process.env.SPIDER_API_KEY! });
const result = await spider.scrapeUrl("https://www.scikit-learn.org", {
return_format: "markdown",
});
console.log(result);Ready for volume? Get an API key →
Fields you can pull.
Spider names these from the page. The capture above came back as markdown; the same
call with return_format: "json" returns them as keys.
What scikit-learn.org costs to scrape.
The capture above cost $0.000136 to fetch. Pricing is $1 per GB of pre-transformation bandwidth plus $0.001 per CPU minute, so a page like this one lands at a fraction of a cent. Failed requests are billed at $0.
- Free balance on signup
- No card required to test
- Balance never expires
Run it keyless, no account
More Education scrapers.
Coursera Scraper
Extract course listings, instructor data, ratings, and enrollment info from Coursera.
Udemy Scraper
Extract course listings, pricing, instructor reviews, and curriculum data from Udemy.
Amazon Books Scraper
Extract bestseller book data, ratings, pricing, and author info from Amazon Books.
Start scraping scikit-learn.org.
You already have the call. A key raises the rate limit and turns on browser rendering, proxies, and concurrency. Balance never expires, and top-ups go through secure checkout.