univer-5th-semester/literature-review
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linear -> logistic regression

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Артём Тищенко
2024-11-04 13:51:46 +03:00
parent 30cf2d2280
commit 557213c004
2 changed files with 2 additions and 2 deletions
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ml_table/ml_table.tex
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@@ -16,7 +16,7 @@
\begin{table}[h!]
\centering
\caption*{\small Table 2. Machine learning algorithms comparison. Algorithms: Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Neural Network (NN), Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA), eXtreme Gradient Boosting (XGB), Generalized Linear Model (GLM), Linear Regression (LR), Cox Regression based algorithms including stepwise Cox, Coxboost, plsRcox (Cox), Supervised Principal Components (SuperPC), Elastic Network (Enet), Gradient Boosting Machine (GBM).}
\caption*{\small Table 2. Machine learning algorithms comparison. Algorithms: Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Neural Network (NN), Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA), eXtreme Gradient Boosting (XGB), Generalized Linear Model (GLM), Logistic Regression (LR), Cox Regression based algorithms including stepwise Cox, Coxboost, plsRcox (Cox), Supervised Principal Components (SuperPC), Elastic Network (Enet), Gradient Boosting Machine (GBM).}
\footnotesize
\begin{tabularx}{\textwidth}{|p{9cm}|X|X|X|X|X|X|X|X|X|X|X|X|X|X|}
\hline