Tag: predicting

Feed: R-bloggers. Author: Part Time Analyst. [This article was first published on Sport Data Science, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Hello, hope you have your Yorkshire tea to hand and sitting comfortably ready to read today’s blog. In it I am going to be doing some machine learning with tidymodels to predict the outcome of some twenty20 cricket matches. I am using the data from cricsheet as used in this ... Read More

Feed: AWS Public Sector Blog. Author: Rafael Monge Vargas. Though it accounts for only 0.03% of the Earth’s surface, Costa Rica is home to about 6% of the world’s biodiversity. Leveraging Costa Rica’s rich biodiversity and advancements in ecosystem modeling, leaders in sustainability are making geospatial data and analysis of Costa Rican landscapes publicly accessible. The data and analysis help people understand how different species influence their ecosystems, while also informing important policy decisions that support the natural world. As part of the Amazon Sustainability Data Initiative (ASDI)—a program that seeks to accelerate sustainability research and innovation by minimizing the ... Read More

Feed: R-bloggers. Author: R | JLaw's R Blog. Normally, I would do some EDA to better understand the data set but in the interest of word count I’ll jump right into using tidymodels to predict whether or not a given field goal attempt will be iced. In order to make the data work with the XGBoost algorithm I’ll subset and convert some numeric variables including our dependent variable to factors. A frustrating thing I learned in writing this post is that with a factor dependent variable the assumption is that the first level is the positive class. I’m recoding is_iced ... Read More

Feed: R-bloggers. Author: jb.hasse. [This article was first published on R-posts.com, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Even if this sounds incredible, yes, we can predict future recessions using a couple of time series, some simple econometric models, and … R ! The basic idea is that the slope of the yield curve is somewhat linked to the probability of future recessions. In other words, the difference between the short and the ... Read More

Feed: Top Blog Posts. Author: Stephanie Glen.   A new study evaluates ML models that classify fake news from fact. The best models can only achieve up to 77.2% accuracy. AI will probably never be able to fully replace the nuanced analysis of human journalists. Many Natural Language Processing (NLP) techniques exist for detecting “fake news”. Multi-phase algorithms with Determined Decision Trees, Gradient Enlargement, and others have been used by various researchers and organizations with varying results. One study from researchers at Rensselaer Polytechnic Institute reported 83% accuracy in predicting whether a news article is from a reliable or unreliable ... Read More

Feed: Top Blog Posts. Author: Stephanie Glen.   A new study evaluates ML models that classify fake news from fact. The best models can only achieve up to 77.2% accuracy. AI will probably never be able to fully replace the nuanced analysis of human journalists. Many Natural Language Processing (NLP) techniques exist for detecting “fake news”. Multi-phase algorithms with Determined Decision Trees, Gradient Enlargement, and others have been used by various researchers and organizations with varying results. One study from researchers at Rensselaer Polytechnic Institute reported 83% accuracy in predicting whether a news article is from a reliable or unreliable ... Read More

Feed: R-bloggers. Author: rstats on Bryan Shalloway's Blog. TLDR: FiveThirtyEight’s forecasts of NBA playoff berths seem to hold-up OK against betting markets. If you trust them, you should consider betting against the Lakers right now. In The Virtues and Vices of Election Prediction Markets Nate Silver explains why FiveThirtyEight generally should not beat the market: “The general question of whether FiveThirtyEight ought to be better than prediction and betting markets is an interesting one. I am far from an efficient-market hypothesis purist, but markets are tough to beat in most circumstances. Furthermore, the FiveThirtyEight forecasts are public information, and bettors ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Stephanie Glen.   A new study evaluates ML models that classify fake news from fact. The best models can only achieve up to 77.2% accuracy. AI will probably never be able to fully replace the nuanced analysis of human journalists. Many Natural Language Processing (NLP) techniques exist for detecting “fake news”. Multi-phase algorithms with Determined Decision Trees, Gradient Enlargement, and others have been used by various researchers and organizations with varying results. One study from researchers at Rensselaer Polytechnic Institute reported 83% accuracy in predicting whether a news article is from ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Kushal Mukherjee. Here, we introduce Bass diffusion model which is a classic way to predict sales for newly launched product in the market. It is an effective way to know the overall sales of the product in its lifecycle and make strategy accordingly. After launching a new product in the market, sales prediction for it has always been a difficult task due to lack of historical data. However, having an accurate prediction is extremely important not only from a marketing standpoint, but also for managing overall product life cycle. It helps ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Bill Schmarzo. I’m overjoyed to announce the release of my latest book “The Economics of Data, Analytics, and Digital Transformation.”  The book takes many of the concepts discussed in this blog to the next level of pragmatic, actionable detail.  Thanks for your support! I recently read where one very profitable on-line company proudly announced that they had “30,000 trained ML models in operations”.  Clearly, this company feels that possessing a large number of ML models is an important indicator of its analytics prowess and maturity.  However, this perpetuates a false narrative; ... Read More