However, Machine Learning (ML) methods can improve stock market predictions to some extent. In this paper, a novel strategy is proposed to improve the prediction efficiency of ML models for financial markets. Nine ML models are used to predict the direction of the ...
Therefore, this work proposes the use of machine learning algorithms to predict if a given set of offsets, following the mandatory rules in force, should be implemented or not. In this manner, this paper presents a case study where a finite element model of an Anchor Handling Tug Supply (...
In this paper, we make a performance comparison of several state-of-the-art machine learning packages on the edges, including TensorFlow, Caffe2, MXNet, PyTorch, and TensorFlow Lite. We focus on evaluating the latency, memory footprint, and energy of these tools with two popular types of ...
performance <- rbind(performance, data.frame(Lambda = lambda, MSE = mse)) } ggplot(performance, aes(x = Lambda, y = MSE)) + geom_point() + scale_x_log10() #有两个lambda对应的错误率是最小的,我们选了较大的那个,因为这意味着更强的正则化 best.lambda <- with(performance, max(Lambda...
Machine Learning for Hackers, Chapter 12: Model Comparison Data Mining: Practical Machine Learning Tools and Techniques, Chapter 7: Transformations: Engineering the input and output The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Chapter 16: Ensemble Learning ...
Studies included in this systematic review have shown that artificial intelligence and machine learning models provide useful insight, despite the heterogeneity of presentation, diagnosis, disease course and patient outcome. However, the heterogeneity in data used, models and model evaluation cause difficult...
Evaluation of machine learning methods for formation lithology identification: A comparison of tuning processes and model performances Author links open overlay panelYunxin Xie a b, Chenyang Zhu c, Wen Zhou b, Zhongdong Li a b, Xuan Liu a b, Mei Tu d...
Precision recall curve of different machine learning models. Abbreviation: LR, logistic regression; CART, classification and regression tree; GBM, gradient boosting machine; ANN, artificial neural network; RF, Random forest; SVM, Support vector machine. Full size image Model performance with a varying...
Machine learning predictive performance The dataset used in this study consisted of collated data from different sources: SICCT test results of a herd, past bTB breakdowns, cattle movements, land cover and climate data. From these data we derived 139 variables characterising herds, including current...
The simulation was carried out using Colaboratory and implemented using several machine learning models such as Linear Regression, KNN, Decision-making tree, and Support Vector Regressor along with error comparison, model interpretation, and cross-validation. Colaboratory or colab is an online open-sourc...