![]() ![]() We feel this is a good balance to keep the subreddit from becoming just an advertising platform. If you're a video creator, you may only post your Let's Play or streams once per month. People are likely to be more interested in your content that way. Generally, be a part of the community you are posting in. ![]() Here are some reddit-wide guidelines for self-promotion. Reddit is a place for discussion, not advertising. No memes, low effort posts, or image macrosĭon't come here only to plug your content If you're posting a rail design, hold a signal in your hand. Tips for making your screenshot better: If you're taking a screenshot of a design, turn on alt mode and inserter arrows. When posting a screenshot, add a comment explaining your image or pointing out what you want people to look at. Think about how your words affect others before saying them.Įxplain your screenshots Take a screenshot (or a video), not a picture/recording with your phone. Every post must be about Factorio or of content you have made that is directly inspired by Factorio (such as fan art). If you want to post about other things, there are subreddits for that. This is a subreddit for the game Factorio. The least squares solution is computed using the singular valueĭecomposition of X.Factorio has never in many years had a sale, is currently not on sale, and is not expected to ever be on sale.įactorio developers: "Not having a sale ever is part of our philosophy." Parameter: when set to True Non-Negative Least Squares are then applied.ġ.1.1.2. LinearRegression accepts a boolean positive Quantities (e.g., frequency counts or prices of goods). It is possible to constrain all the coefficients to be non-negative, which mayīe useful when they represent some physical or naturally non-negative This situation of multicollinearity can arise, forĮxample, when data are collected without an experimental design. To random errors in the observed target, producing a large When features are correlated and theĬolumns of the design matrix \(X\) have an approximately linearĭependence, the design matrix becomes close to singularĪnd as a result, the least-squares estimate becomes highly sensitive The coefficient estimates for Ordinary Least Squares rely on the ![]() from sklearn import linear_model > reg = linear_model. ![]()
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