onfirのブログ -11ページ目

onfirのブログ

ブログの説明を入力します。

In a recent report, Ben Hamner told us about the common misconceptions that he and his colleagues saw the game in Kaggle Air Max for Women some machine learning projects. The report in February 2014 held at Strate, called 'machine learning elf.' In Air Jordan 13 this article, we will report Ben understand some common myths, what they are and how to avoid falling into these errors. Machine learning process before the report, Ben showed us a machine learning problem solving process in general. Machine learning process, taken from Ben Hamner 'machine learning elf' This process 2012 Cheap Nike Air Jordan 1 High Heels For Sale Beige Pink includes the following nine steps: Start with a business problem data segmentation data source selection evaluation criteria a training model for feature extraction Nike shoes online feature selection model to select 180-159220 Nike LeBron 7 VII Soldier White Black Running Shoes Air Jordan 1 production system Ben emphasis on this process It is an iterative process, and non-linear. He also talked about in this process can go wrong every step, every error can make the entire machine learning process is difficult to achieve the desired results. Identification of dog and cat Ben proposed a study Nike Shoes to build a 'cat door automatic' case, this 'door' is open to cats and dogs close. This is an instructive example, because it is designed to deal with a number of key issues of data on the issue. Identification of dogs and cats, taken from Ben Hamner 'machine learning elf' sample size of the first selling point of this example is that the accuracy of model study sample size and data related to and show more samples with better accuracy of Relations between. Through his increasing training data, until the model accuracy is stabilized. This can be a good example to let you know that your system Air Jordan 11 is adjusted to the sample size and how sensitive. Wrong Air Jordan 12 question second selling point is the system fails, it all cats are turned away. This example highlights the understanding we need to solve the problem of constraint is very important, instead of focusing on issues you want to solve. Machine Learning Project Mistakes Ben then discussed the problem of machine learning to solve the four common misconceptions. Although these problems 2013 Air Jordan 4 (IV) Retro are very common, but he pointed out that they are relatively easy to identify and resolve. Overfitting, taken from Ben Hamner 'machine learning elf' Data leaks: using the model in production systems can not access data. In this issue of timing issues particularly common. May also occur in the image data system id, id may represent a class label. Run the model and carefully review the features of the system help. Complete check and consider whether it makes sense. (Check the reference paper 'data mining disclosure | Leakage in Data Mining'). Overfitting: on the training data modeling too sophisticated, but the model has some noises. Then over the expansion of capacity will be reduced to fit the model, its even worse in the higher dimensions Lebron James Shoes and more complex class boundaries. Air Jordans Men's Adoption and segmentation data: Compared to data leakage, you need to be very careful to know the training, testing, and crosscheck whether the Air Max for Men data Nike Shoes Global set is truly independent data sets. For timing issues, a lot of ideas and work need to ensure that the system can respond to the chronological accuracy of the data and validation of the model. Data Quality: Check the consistency of your data. Ben gave a flight landing sites and data, many inconsistencies, duplicate and erroneous data clearly need to be identified and treated. These data will directly harm scalable modeling and model. Ben summary 'machine learning elf' is a quick and useful reports. You will get a common misunderstanding about Air Jordan 10 the usefulness of machine learning accelerated learning, and these skills can be easily used in a work processing data among.machine learning projects common errors