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19.3 Supervised Learning In supervised learning the data set includes the truth, by providing a machine learning algorithm, a training data set with observations for both independent variables (eg, X1, X2, etc.), and a dependent variable (eg, Y), so that for each observation of independent ...
本文对machine learning testing做了比较全面的调研,其内容:涵盖测试属性(如正确性、鲁棒性、公平性)、测试组件(如数据、学习程序、框架)、测试工作流(如测试生成、测试评估)、应用场景(如自动驾驶、机器翻译)等等。分析了机器学习测试,数据集发展趋势、研究趋势和研究重点,提出了机器学习测试的研究挑战和发展方向。
If a call to the training or prediction functions of a machine learning library instead yields unexpected exceptions, this is a clear indicator of a bug in the most crucial functions. We developed a set of smoke tests that we believe all machine learning algorithms must be able to pass. The...
5.4.1.2Human impairment data set The objective of the second data set is to consider the different characteristics of users. In this data set, a set of gender and language factors is used to investigate the accuracy of each speech prediction model. The structure of this data set is shown i...
1. Model testing: In this step, we evaluate the performance of the trained model on a separated set of data points named test data (which was split and versioned in the data ingestion step). The inference of the trained model is evaluated according to selected metrics as per...
In machine learning, classification task is considered to be a supervised learning algorithm, i.e. learning process uses a training set of elements classified correctly. There is a number of known classification algorithms. One of them is k − N N (k nearest neighbours, where k is a ...
We can formalize this in code: expected_cols = set(expected.columns) actual_cols = set(actual.columns) column_coverage = len(expected_cols & actual_cols) / len(expected_cols) # column_coverage = 1.0Copy For tables with many columns, we may only need say 90% coverage to pass the test...
Hinton, G., Srivastava, N., and Swersky, K. (2012), “Neural Networks for Machine Learning,”Coursera, video lectures, 264, 1. Google Scholar Ho, T. K. (1995), “Random Decision Forests,” in IEEE Proceedings of 3rd International Conference on Document Analysis and Recognition, 1, 278...
Creating a training and testing set As with most supervised learning tasks, we will create a split in our dataset so that weteacha model on one subset and then test its ability to generalize on new data against the holdout set. For the purposes of this example, we split the data 80/20...