below. Below is the list of models included in themldashpackage. Note that models that begin withtm_are models implemented with thetidymodelsR package; models that begin withweka_are models implemented with the theRWekawhich is a wrapper to theWekacollection of machine learning algorithms. ...
In this study, the performance of four machine learning algorithms (MLAs), namely Random Forests (RFs), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Nave Bayes (NB), were investigated to classify the catchment into nine relevant classes of the undulating wat...
It is important problems to increase the detection rates and reduce the false positive rates in network intrusion detection systems (NIDS). In this paper, we propose machine learning algorithms such as Random Forest and AdaBoost, along with Nave Bayes, to build an efficient intrusion detection ...
A new prospective for Learning Automata: A machine learning approach In the field of Learning Automata (LA), how to design faster learning algorithms has always been a key issue. Among solutions reported in the literature, t... J Wen,B Li,S Li,... - 《Neurocomputing》 被引量: 4发表:...
Keywords: cancer; deep learning; drug sensitivity; learned representations; molecular fingerprints 1 Introduction ML has been widely used in the pharmaceutical industry for rational drug discovery. Quantitative structure-activity relationship (QSAR) models, for example, typically use ML algorithms to learn...
The Leave-One-Out Cross-Validation, or LOOCV, procedure is used to estimate the performance of machine learning algorithms when they are used to make predictions on data not used to train the model. It is a computationally expensive procedure to perform, although it results in a reliable and ...
Machine learning (ML) algorithms utilizing electronic health records and survey data offer promising tools for forecasting these conditions. However, potential bias and inaccuracies inherent in subjective survey responses can undermine the precision of such predictions. This research investigates the ...
land subsidence modeling; classification; machine learning algorithms; Semnan plain; Kashmar Plain1. Introduction Land subsidence (LS) is a global environmental issue caused by natural (e.g., earthquakes) or human-induced processes (e.g., over-exploitation of groundwater, dissolution of calcareous ...
How to use the scikit-learn machine learning library to perform the train-test split procedure. How to evaluate machine learning algorithms for classification and regression using the train-test split. Do you have any questions? Ask your questions in the comments below and I will do my best to...
ML output revealed that NuSVR outperformed other algorithms in accurately predicting outcomes during both the training and testing stages. Moreover, Scenario 2 (SC2) with seven selected features from the RFE method facilitated highly accurate SAR predictions. Overall, the performance of ML models is...