Firstly, let's understand ROC (Receiver Operating Characteristic curve) curve.ROC represents a graph to show the performance of a classification model at different threshold levels. The curve is plotted between two parameters, which are: True Positive Rate False Positive Rate TPR or true Positive r...
The main goal of the study was to develop a typology that will help to improve our knowledge and understanding of metrics and facilitate their selection in machine learning regression, forecasting and prognostics. Based on the analysis of the structure of numerous performance metrics, we propose a...
In line with the stated above, the main contribution of this study is to implement machine learning techniques in order to classify a determined set of bearing's offsets returns “great”, “good”, “regular”, or “poor” results, following the rules of shaft line system static alignment ...
Many of them are provided in a way that does not permit changing the regression model, although a number of control parameters can be adjusted to tailor the SF to a particular target. Importantly, the underlying linear regression model employed by classical SFs has been shown to be unable to...
(IDS), which is trained with some machine learning techniques by using a pre-collected dataset, is one of the most preferred protection mechanisms. The used datasets were collected during a limited period in some specific networks and generally don't contain up-to-date data. Additionally, they...
In machine learning, we use the term parameters to refer to something that can be learned by the algorithm during training and hyperparameters to refer to something that is passed to the algorithm. For example: The number of neighbors to inspect in a KNN model is a hyperp...
In this paper, we approach this fundamental problem using machine learning techniques first to generate performance models for all tasks and then applying those models to perform automatic performance prediction across program executions. We also extend an existing scheduling algorithm to use generated ...
This thesis examines the application of machine learning algorithms to predict whether a student will be successful or not. The specific focus of the thesis is the comparison of machine learning methods and feature engineering techniques in terms of how much they improve the prediction performance. ...
Machine Learning Based Classification Approach for Predicting Students Performance in Blended Learning Nowadays, recognizing and predicting students learning achievement introduces a significant challenge, especially in blended learning environments, where o... CG Nespereira,E Elhariri,N El-Bendary,... - ...
For very small micro-benchmarks, inspecting machine instructions and making an estimate based on the number of instructions executed is an excellent check. In a debugger like Visual Studio, it should be as easy as setting a breakpoint in your benchmark code and switching to the disassembly ...