The Support Vector Machine (SVM) is an efficient tool in machine learning with high accuracy performance. However, in order to achieve the highest accuracy performance, n-fold cross validation is commonly used to identify the best hyperparameters for SVM. This becomes a weak point of SVM due to the extremely long training time for various hyperparameters of different kernel functions. In this p...

Identifying the association between metabolites and diseases will help us understand pathogenesis of diseases, which has great significance in diagnosing treating diseases. However, traditional biometric methods are time consuming expensive. Accordingly, we propose a new metabolite-disease prediction algorithm based on DeepWalk random forest (DWRF), consists following key steps: First, semantic...

The volume of distribution (VD) is one of the most important pharmacokinetic parameters of drugs. The present study employs quantitative structure-pharmacokinetics relationships (QSPkR) to derive models for VD prediction of acidic drugs. The steady-state volume of distribution (VD(ss)) values of 132 acidic drugs were collected, the chemical structures were described by 178 molecular descriptors...

Nitrogen (N) and phosphorus (P) in topsoils are critical for plant nutrition. Relatively little is known about the spatial patterns of N and P in the organic layer of mountainous landscapes. Therefore, the spatial distributions of N and P in both the organic layer and the A horizon were analyzed using a light detection and ranging (LiDAR) digital elevation model and vegetation metrics. The obje...

In this paper, we derive nonasymptotic error bounds for the Lasso estimator when penalty parameter is chosen using K-fold cross-validation. Our imply that cross-validated has nearly optimal rates of convergence in prediction, L2, and L1 norms. For example, show model with Gaussian noise under fairly general assumptions on candidate set values parameter, estimation converges to zero prediction n...

This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...

Classification trees (J48) were induced to predict the habitat requirements of tench (Tinca tinca). 306 datasets were used for the given fish during 8 years in the river basins in Flanders (Belgium). The input variables consisted of the structural-habitat (width, depth, gradient slope and distance from the source) and physic chemical (pH, dissolved oxygen, water temperature and electric conduct...

I present two related commands, r_ml_stata_cv and c_ml_stata_cv, for fitting popular machine learning methods in both a regression classification setting. Using the recent Stata/Python integration platform introduced Stata 16, these commands provide hyperparameters’ optimal tuning via K-fold cross-validation using grid search. More specifically, they use Python Scikitlearn application programmi...