官术网_书友最值得收藏!

Compensating factors in machine learning models

Compensating factors in machine learning models to equate statistical diagnostics is explained with the example of a beam being supported by two supports. If one of the supports doesn't exist, the beam will eventually fall down by moving out of balance. A similar analogy is applied for comparing statistical modeling and machine learning methodologies here.

The two-point validation is performed on the statistical modeling methodology on training data using overall model accuracy and individual parameters significance test. Due to the fact that either linear or logistic regression has less variance by shape of the model itself, hence there would be very little chance of it working worse on unseen data. Hence, during deployment, these models do not incur too many deviated results.

However, in the machine learning space, models have a high degree of flexibility which can change from simple to highly complex. On top, statistical diagnostics on individual variables are not performed in machine learning. Hence, it is important to ensure the robustness to avoid overfitting of the models, which will ensure its usability during the implementation phase to ensure correct usage on unseen data.

As mentioned previously, in machine learning, data will be split into three parts (train data - 50 percent, validation data - 25 percent, testing data - 25 percent) rather than two parts in statistical methodology. Machine learning models should be developed on training data, and its hyperparameters should be tuned based on validation data to ensure the two-point validation equivalence; this way, the robustness of models is ensured without diagnostics performed at an individual variable level:

Before diving deep into comparisons between both streams, we will start understanding the fundamentals of each model individually. Let us start with linear regression! This model might sound trivial; however, knowing the linear regression working principles will create a foundation for more advanced statistical and machine learning models. Below are the assumptions of linear regression.

主站蜘蛛池模板: 南乐县| 油尖旺区| 深水埗区| 利津县| 灵川县| 阿图什市| 仙居县| 龙井市| 禄丰县| 新绛县| 凤山市| 眉山市| 阜南县| 阿克苏市| 彭山县| 通江县| 潼关县| 宝清县| 霍州市| 汝州市| 元谋县| 松原市| 永安市| 长宁区| 象山县| 宝山区| 罗甸县| 石门县| 嘉定区| 巴林左旗| 遂川县| 连江县| 股票| 南江县| 昌宁县| 纳雍县| 平潭县| 汝城县| 天柱县| 普兰店市| 庄浪县|