官术网_书友最值得收藏!

Comparison between regression and machine learning models

Linear regression and machine learning models both try to solve the same problem in different ways. In the following simple example of a two-variable equation fitting the best possible plane, regression models try to fit the best possible hyperplane by minimizing the errors between the hyperplane and actual observations. However, in machine learning, the same problem has been converted into an optimization problem in which errors are modeled in squared form to minimize errors by altering the weights.

In statistical modeling, samples are drawn from the population and the model will be fitted on sampled data. However, in machine learning, even small numbers such as 30 observations would be good enough to update the weights at the end of each iteration; in a few cases, such as online learning, the model will be updated with even one observation:

Machine learning models can be effectively parallelized and made to work on multiple machines in which model weights are broadcast across the machines, and so on. In the case of big data with Spark, these techniques are implemented.

Statistical models are parametric in nature, which means a model will have parameters on which diagnostics are performed to check the validity of the model. Whereas machine learning models are non-parametric, do not have any parameters, or curve assumptions; these models learn by themselves based on provided data and come up with complex and intricate functions rather than predefined function fitting.

Multi-collinearity checks are required to be performed in statistical modeling. Whereas, in machine learning space, weights automatically get adjusted to compensate the multi-collinearity problem. If we consider tree-based ensemble methods such as bagging, random forest, boosting, and so on, multi-collinearity does not even exist, as the underlying model is a decision tree, which does not have a multi-collinearity problem in the first place.

With the evolution of big data and distributed parallel computing, more complex models are producing state-of-the-art results which were impossible with past technology.

主站蜘蛛池模板: 城口县| 荣成市| 渑池县| 揭东县| 贵南县| 松滋市| 大竹县| 肃北| 都兰县| 嘉义市| 子长县| 白玉县| 曲沃县| 永善县| 五指山市| 浏阳市| 安远县| 安国市| 德昌县| 阳江市| 天全县| 咸丰县| 托克逊县| 湘潭市| 岳阳县| 庆阳市| 贞丰县| 彩票| 绿春县| 马鞍山市| 大石桥市| 长葛市| 肥乡县| 卓尼县| 繁昌县| 涿州市| 田东县| 黑河市| 华坪县| 鹤岗市| 温泉县|