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: 辰溪县| 南平市| 凤山市| 秀山| 中卫市| 陵川县| 双江| 姚安县| 泰安市| 刚察县| 休宁县| 乌兰浩特市| 包头市| 南丹县| 定远县| 共和县| 株洲市| 石柱| 喀喇沁旗| 西林县| 藁城市| 涡阳县| 扶风县| 新余市| 叙永县| 札达县| 板桥市| 阿尔山市| 台北市| 潞城市| 湘潭县| 临江市| 甘南县| 理塘县| 小金县| 广西| 金塔县| 历史| 临猗县| 渭南市| 谷城县|