官术网_书友最值得收藏!

Defining problem and objectives

Before any development begins, the problem to be solved must be defined together with objectives of what good will look like, to set expectations. The way the problem is formulated is very important, as this can mean the difference between intractability and a simple solution. It is also likely to involve a conversation about where the input data for any algorithm will come from.

ML algorithms usually require large amounts of data to perform at their best. Sourcing quality data is the most important consideration when planning a ML project. 

The typical formulation of an ML problem takes the form given X dataset, predict Y. The availability of data or lack of it thereof can affect the formulation of the problem, the solution, and its feasibility. For example, consider the problem given a large labeled set of images of handwritten digits[18], predict the label of a previously unseen image. Deep learning algorithms have demonstrated that it is possible to achieve relatively high accuracy on this particular problem with little work on the part of the engineer, as long as the training dataset is sufficiently large[19]. If the training set is not large, the problem immediately becomes more difficult and requires a careful selection of the algorithm to use. It also affects the accuracy and thus, the set of attainable objectives.

Experiments performed by Michael Nielsen on the MNIST handwritten digit dataset show that the difference between training an ML algorithm with 1 example of labeled input/output pairs per digit and 5 examples was an improvement of accuracy from around 40% to around 65% for most algorithms tested[20]. Using 10 examples per digit usually raised the accuracy a further 5%.

If insufficient data is available to meet the project objectives, it is sometimes possible to boost performance by artificially expanding the dataset by making small changes to existing examples. In the previously mentioned experiments, Nielsen observed that adding slightly rotated or translated images to the dataset improved performance by as much as 15%.

主站蜘蛛池模板: 肃宁县| 福清市| 南京市| 高台县| 巴彦县| 拜城县| 甘孜县| 紫云| 大石桥市| 疏勒县| 金沙县| 上林县| 宁阳县| 克东县| 大关县| 涟水县| 井研县| 恭城| 宿迁市| 龙川县| 宾川县| 古蔺县| 茌平县| 永城市| 临泉县| 昌吉市| 开封县| 韶山市| 房山区| 九寨沟县| 泰来县| 原阳县| 孟津县| 富平县| 唐海县| 米泉市| 建水县| 泰来县| 苗栗县| 凤山县| 三江|