Sentence examples for decision forest classifier from inspiring English sources

We then learn the correlation between the motion of two feet using the probabilistic trajectories in a decision forest classifier.

For the baseline method, extraversion and leadership classification models were constructed using support vector machine, decision forest and ridge regression classifiers.

Several classifiers were tested: decision tree, logistic regression, support vector machine (SVM), random forest classifier and Extreme Gradient Boosting (XGBoost) [36], using Scikit-learn toolkit [37].

In Section 2.1, we will introduce the random forest classifier by describing the training of a decision tree and then explain how this tree is used in the random forest.

Using MRMR for feature selection and PCA for feature extraction alongside classifiers such as SVMs, ridge regression, and decision forest, we evaluated our transfer and multi-task learning-based personality prediction framework.

For each GO term, we trained a separate random forest classifier as an ensemble of 100 recursive partitioning or decision tree classifiers, using the GBP feature set and the training set of genes as gold-standard positive examples.

The performance of the random forest classifier can be further improved by increasing the number of decision trees in the ensemble.

The random forest classifier was chosen due to its superior performance over a single decision tree with respect to accuracy.

The comparative analysis of the proposed classifiers has demonstrated that the best results are achieved by a decision forest made of 30 trees.

In previous work,40 we built and compared a logistic regression classifier with the random forest classifier.

In the supplementary materials, under Random Forest Classifier Error Analysis, we provide the classifier error analysis and AUROC and AUPRC plots for clarity.