3.1 Dataset Prepare 16 3.2 Feature extraction 16 3.2.1 Energy or scoring function (7 features) 16 3.2.2 Protein secondary structure (5 features) 16 3.2.3 Solvent accessibility (3 features) 18 3.3 Parameters Optimization of Machine Learning Methods 18 3.3.1 Linear Model 19 3.3.2 Decision Tree 19 3.3.3 Neural Network 19 3.3.4 Boosting 20 3.3.5 Random Forest 20 Chapter 4: EXPERIMENTAL Results 21 4.1 Data Set 21 4.2 Evaluation Parameters 22 4.2.1 Pearson’s correlation 22 4.2.2 Quality score 23 4.2.3 Quality lost 23 4.3 Features’ Correlation Coefficient 23 4.4 CASP10 model 26 4.4.1 Linear Model 30 4.4.2 Decision Tree 31 4.4.3 Neural Network 33
3.1 Dataset Prepare 16 3.2 Feature extraction 16 3.2.1 Energy or scoring function (7 features) 16 3.2.2 Protein secondary structure (5 features) 16 3.2.3 Solvent accessibility (3 features) 18 3.3 Parameters Optimization of Machine Learning Methods 18 3.3.1 Linear Model 19 3.3.2 Decision Tree 19 3.3.3 Neural Network 19 3.3.4 Boosting 20 3.3.5 Random Forest 20 Chapter 4: EXPERIMENTAL Results 21 4.1 Data Set 21 4.2 Evaluation Parameters 22 4.2.1 Pearson’s correlation 22 4.2.2 Quality score 23 4.2.3 Quality lost 23 4.3 Features’ Correlation Coefficient 23 4.4 CASP10 model 26 4.4.1 Linear Model 30 4.4.2 Decision Tree 31 4.4.3 Neural Network 33