We tested four models: logistic regression (LogReg),
multilayer perceptron (MLP), graph convolutional network
(GCN), and edge-attention graph convolutional network
(EAGCN).
Are there other models for machine learning test that we can observe the performance of a software using multiple ligand recognition and docking scores?
Yes, you can observe SVM, Naive Bayes, Decision tree and Random forest.
I don't know your dataset and whether it fits your data or not ..but you may try these too : Artificial Neural Network (ANN), Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) .
Yes, you can observe SVM, Naive Bayes, Decision tree and Random forest.
MAchine Learning and deep Leraning are different :
DEEP LEARNİNG METHODS:
LOGISTIC REGRESSION
ARTIFICIAL NEURAL NETWORK (ANN)
CONVOLUTIONAL NEURAL NETWORK (CNN)
RECURRENT NEURAL NETWORK (RNN)
LONG SHORT TERM MEMORY
MACHINE LEARNING METHODS:
ALL DEEP LEARNNIG METHODS ABOVE AND ALSO +
1. Regression:
- Linear Regression
- Multiple Linear Regression
- Polynomial Linear Regression
- Decision Tree Regression
- Random Forest Regression
- Evaluation Regression Models
2. Classification:
- Logistic Regression
- K-Neirest Neighbour (KNN)
- Support Vector Machine (SVM)
- Naive Bayes
- Decision Tree
- Random Forest
- Evaluation Classification Models
3. Unsupervised Learning:
- Clustering
- K-Means
- Hierarchical Clustering
4. Natural Language Process (NLP)
5. Principle Component Analysis (PCA)
6. Model Selection:
- K-Fold Cross Validation
- Grid Search
- Badges
- Science topic
- Similar topics
- Mathematical Sciences
- Graphs