no-free-lunch.org valuation and analysis
Robots.txt Information
| Robot | Path | Permission |
| GoogleBot | / | ✔ |
| BingBot | / | ✔ |
| BaiduSpider | / | ✔ |
| YandexBot | / | ✔ |
Meta Tags
| Title | No Free Lunch |
| Description | No free lunch |
| Keywords | no free lunch, no free lunch theorem, no free lunch theorems, free lunch, Wolpert, Macready, learning, search, optimization |
Server Information
| WebSite | |
| Host IP | 79.170.44.88 |
| Location | United Kingdom |
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no-free-lunch.org Valuation
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US$3,512,888
Last updated: 2023-05-14 17:43:37
no-free-lunch.org has Semrush global rank of 3,012,992. no-free-lunch.org has an estimated worth of US$ 3,512,888, based on its estimated Ads revenue. no-free-lunch.org receives approximately 405,334 unique visitors each day. Its web server is located in United Kingdom, with IP address 79.170.44.88. According to SiteAdvisor, no-free-lunch.org is safe to visit. |
Traffic & Worth Estimates
| Purchase/Sale Value | US$3,512,888 |
| Daily Ads Revenue | US$3,243 |
| Monthly Ads Revenue | US$97,280 |
| Yearly Ads Revenue | US$1,167,360 |
| Daily Unique Visitors | 27,023 |
| Note: All traffic and earnings values are estimates. | |
DNS Records
| Host | Type | TTL | Data |
| no-free-lunch.org. | A | 599 | IP: 79.170.44.88 |
| no-free-lunch.org. | NS | 3600 | NS Record: ns64.domaincontrol.com. |
| no-free-lunch.org. | NS | 3600 | NS Record: ns63.domaincontrol.com. |
| no-free-lunch.org. | MX | 3600 | MX Record: 10 mail.no-free-lunch.org. |
HtmlToTextCheckTime:2023-05-14 17:43:37
| No Free Lunch Theorems Broadly speaking, there are two no free lunch theorems. One for supervised machine learning (Wolpert 1996) and one for search/optimization (Wolpert and Macready 1997). For an overview of the (no) free lunch and associated theorems, see David Wolpert’s What does dinner cost? No Free Lunch for Supervised Machine Learning Hume (1739–1740) pointed out that ‘even after the observation of the frequent or constant conjunction of objects, we have no reason to draw any inference concerning any object beyond those of which we have had experience’. More recently, and with increasing rigour, Mitchell (1980), Schaffer (1994) and Wolpert (1996) showed that bias-free learning is futile. Wolpert (1996) shows that in a noise-free scenario where the loss function is the misclassification rate, if one is interested in off-training-set error, then there are no a priori distinctions between learning algorithms. More formally, where d = training set; m = number of elements in |
HTTP Headers
HTTP/1.1 200 OK date: Thu, 23 Dec 2021 19:40:27 GMT server: Apache/2.4.51 (Unix) last-modified: Sun, 12 Jun 2011 18:01:43 GMT etag: "4b9d-4a58798688fc0" accept-ranges: bytes content-length: 19357 content-type: text/html |
no-free-lunch.org Whois Information
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