Deep learning example with DeepExplainer (TensorFlow/Keras models) Deep SHAP is a high-speed approximation algorithm for SHAP values in deep learning models that builds on a connection withDeepLIFTdescribed in the SHAP NIPS paper. The implementation here differs from the original DeepLIFT by using a...
Computer networks are a bit like the army: everything in a network has to be arranged with almost military precision and it has to behave according to very clearly defined rules. In a LAN, for example, you can't connect things together any old how: all the nodes (computers and other ...
The DNN model for this example imitates an automotive lane keeping assist (LKA) system implemented using model predictive control (MPC). A vehicle (ego car) equipped with an LKA system has a sensor, such as camera, that measures the lateral deviation and relative yaw angle between the centerli...
The created decision tree can easily be visualized and thus the algorithm's results be compared and validated. Nonetheless, the approach and ismodel agnostic, which means any other model could be explained. This also includes such that are not inherently visualizable. ...
The optimizer uses the smaller of the two tables or data sources to build a hash table, based on the join key, in memory. It then scans the larger table, and performs the same hashing algorithm on the join column(s). It then probes the previously built hash table for each value and ...
The longest match number string dial-peer algorithm finds the dial-peer with the most numbers in a sequence that exactly match a sequence of numbers in a number string. This concept is clarified in the subsequent scenario. Scenario: Eligible dial-peers have been configured with t...
A value of 'TQ PUSHDOWN' indicates that the bit filter operation has been pushed down. This argument will not be included at all when the optimizer does not use a pushdown with the hash join. GREEDY TRUE Indicates whether the optimizer used a greedy algorithm to plan access. GLOBLOCK ...
There is a problem with this argument. What you have shown is that if there exists an algorithm A that solves the halting problem, then there is an algorithm B that solves the "all-input-halting-problem". However, the all-input-halting-problem is harder in the following sense: Given an...
For example, if we created one decision tree, the third one, it would predict 0. But if we relied on the mode of all 4 decision trees, then the predicted value would be 1. This is the power of random forests. AdaBoost AdaBoost is a boosted algorithm that is similar to Random Forest...
The eigenvalues were additionally analyzed using a knee-point detec- tion algorithm available via the 'SamSPECTRAL' R package (Zare et al. 2015), which also suggested a 2-factor solution. A 2-factor factor analysis with varimax rotation was conducted to evaluate which dis- patch categories ...