官术网_书友最值得收藏!

Classification algorithms

One of the popular subsets of ML algorithms is classification algorithms. They are also referred to as supervised learning algorithms. For this approach, we assume that we have a rich dataset of features and events associated with those features. The task of the algorithm is to predict an event given a set of features. The event is referred to as a class variable. For example, consider the following dataset of features related to weather and whether it snowed on a particular day:

Table 1: Sample dataset

 

In the dataset, a weather station has information about the temperature, the sky condition, and the wind speed for the day. They also have records of when they received snowfall. The classification problem they are working on is to predict snowfall based on features such as temperature, sky condition, and wind speed.

Let's discuss some terminology that is used in ML datasets. In the example, if the classification problem is to predict snowfall, then the snowfall feature is referred to as a class or target variable. Non-class values are referred to as attribute or feature variables. Each row in this dataset is referred to as an observation.

主站蜘蛛池模板: 永安市| 若羌县| 垣曲县| 大厂| 工布江达县| 桐城市| 永福县| 楚雄市| 赞皇县| 寻甸| 楚雄市| 辽阳市| 武定县| 新干县| 平定县| 镇远县| 招远市| 迁安市| 平舆县| 福州市| 图木舒克市| 阳谷县| 临泽县| 松溪县| 旬邑县| 郧西县| 金寨县| 奇台县| 平谷区| 丰城市| 萍乡市| 象山县| 普宁市| 南和县| 额济纳旗| 安福县| 原阳县| 新田县| 襄汾县| 黄骅市| 舟曲县|