{"id": "machine-learning-textbook_T1_164", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Recall?", "ground_truth": ["Recall", "EVAL"], "concept_id": 164, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_29", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Leaf Node?", "ground_truth": ["Leaf Node", "TREE"], "concept_id": 29, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_7", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Test Data?", "ground_truth": ["Test Data", "FOUND"], "concept_id": 7, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_190", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Momentum?", "ground_truth": ["Momentum", "OPT"], "concept_id": 190, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_71", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Centroid?", "ground_truth": ["Centroid", "CLUST"], "concept_id": 71, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_63", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Kernel Trick?", "ground_truth": ["Kernel Trick", "SVM"], "concept_id": 63, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_58", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Support Vectors?", "ground_truth": ["Support Vectors", "SVM"], "concept_id": 58, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_36", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Underfitting?", "ground_truth": ["Underfitting", "TREE"], "concept_id": 36, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_189", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is RMSprop?", "ground_truth": ["RMSprop", "OPT"], "concept_id": 189, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_27", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Decision Tree?", "ground_truth": ["Decision Tree", "TREE"], "concept_id": 27, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_174", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Z-Score Normalization?", "ground_truth": ["Z-Score Normalization", "PREP"], "concept_id": 174, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_140", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Pre-Trained Model?", "ground_truth": ["Pre-Trained Model", "FOUND"], "concept_id": 140, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_23", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Curse of Dimensionality?", "ground_truth": ["Curse of Dimensionality", "KNN"], "concept_id": 23, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_152", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Generalization?", "ground_truth": ["Generalization", "EVAL"], "concept_id": 152, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_109", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Hidden Layer?", "ground_truth": ["Hidden Layer", "NN"], "concept_id": 109, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_9", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Feature?", "ground_truth": ["Feature", "FOUND"], "concept_id": 9, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_8", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Validation Data?", "ground_truth": ["Validation Data", "FOUND"], "concept_id": 8, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_24", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is KNN for Classification?", "ground_truth": ["KNN for Classification", "KNN"], "concept_id": 24, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_56", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Hyperplane?", "ground_truth": ["Hyperplane", "SVM"], "concept_id": 56, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_60", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Hard Margin SVM?", "ground_truth": ["Hard Margin SVM", "SVM"], "concept_id": 60, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_130", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Translation Invariance?", "ground_truth": ["Translation Invariance", "CNN"], "concept_id": 130, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_155", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Stratified Sampling?", "ground_truth": ["Stratified Sampling", "EVAL"], "concept_id": 155, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_198", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Grid Search?", "ground_truth": ["Grid Search", "OPT"], "concept_id": 198, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_144", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is ImageNet?", "ground_truth": ["ImageNet", "TL"], "concept_id": 144, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_51", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is L1 Regularization?", "ground_truth": ["L1 Regularization", "REG"], "concept_id": 51, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_167", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is AUC?", "ground_truth": ["AUC", "EVAL"], "concept_id": 167, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_192", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Gradient Clipping?", "ground_truth": ["Gradient Clipping", "OPT"], "concept_id": 192, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_108", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Fully Connected Layer?", "ground_truth": ["Fully Connected Layer", "NN"], "concept_id": 108, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_57", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Margin?", "ground_truth": ["Margin", "SVM"], "concept_id": 57, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_115", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Universal Approximation?", "ground_truth": ["Universal Approximation", "NN"], "concept_id": 115, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_151", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Test Error?", "ground_truth": ["Test Error", "EVAL"], "concept_id": 151, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_72", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Cluster Assignment?", "ground_truth": ["Cluster Assignment", "CLUST"], "concept_id": 72, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_2", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Supervised Learning?", "ground_truth": ["Supervised Learning", "FOUND"], "concept_id": 2, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_41", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Logistic Regression?", "ground_truth": ["Logistic Regression", "MISC"], "concept_id": 41, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_186", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Online Learning?", "ground_truth": ["Online Learning", "OPT"], "concept_id": 186, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_88", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Sigmoid Activation?", "ground_truth": ["Sigmoid Activation", "LOGREG"], "concept_id": 88, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_169", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Specificity?", "ground_truth": ["Specificity", "EVAL"], "concept_id": 169, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_40", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Feature Space Partitioning?", "ground_truth": ["Feature Space Partitioning", "FOUND"], "concept_id": 40, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_182", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Time Complexity?", "ground_truth": ["Time Complexity", "OPT"], "concept_id": 182, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_87", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Tanh?", "ground_truth": ["Tanh", "NN"], "concept_id": 87, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_191", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Nesterov Momentum?", "ground_truth": ["Nesterov Momentum", "OPT"], "concept_id": 191, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_183", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Space Complexity?", "ground_truth": ["Space Complexity", "OPT"], "concept_id": 183, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_98", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Loss Function?", "ground_truth": ["Loss Function", "NN"], "concept_id": 98, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_25", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is KNN for Regression?", "ground_truth": ["KNN for Regression", "KNN"], "concept_id": 25, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_92", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Forward Propagation?", "ground_truth": ["Forward Propagation", "NN"], "concept_id": 92, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_89", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Leaky ReLU?", "ground_truth": ["Leaky ReLU", "NN"], "concept_id": 89, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_68", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Dual Formulation?", "ground_truth": ["Dual Formulation", "SVM"], "concept_id": 68, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_12", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Feature Vector?", "ground_truth": ["Feature Vector", "FOUND"], "concept_id": 12, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_118", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Filter?", "ground_truth": ["Filter", "CNN"], "concept_id": 118, "hop_depth": 0}
{"id": "machine-learning-textbook_T1_138", "domain": "machine-learning-textbook", "type": "T1_entity", "query": "What is Inception?", "ground_truth": ["Inception", "CNN"], "concept_id": 138, "hop_depth": 0}
{"id": "machine-learning-textbook_T2_33", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Gini Impurity?", "ground_truth": ["Splitting Criterion"], "concept_id": 33, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_98", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Loss Function?", "ground_truth": ["Model"], "concept_id": 98, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_22", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Voronoi Diagram?", "ground_truth": ["K-Nearest Neighbors", "Decision Boundary"], "concept_id": 22, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_143", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Domain Adaptation?", "ground_truth": ["Transfer Learning"], "concept_id": 143, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_77", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Elbow Method?", "ground_truth": ["K-Means Clustering", "Hyperparameter"], "concept_id": 77, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_162", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Accuracy?", "ground_truth": ["Confusion Matrix"], "concept_id": 162, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_160", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for True Negative?", "ground_truth": ["Confusion Matrix"], "concept_id": 160, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_94", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Gradient Descent?", "ground_truth": ["Backpropagation"], "concept_id": 94, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_149", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Training Error?", "ground_truth": ["Training Data", "Model"], "concept_id": 149, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_51", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for L1 Regularization?", "ground_truth": ["Regularization"], "concept_id": 51, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_182", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Time Complexity?", "ground_truth": ["Computational Complexity"], "concept_id": 182, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_19", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Manhattan Distance?", "ground_truth": ["Distance Metric"], "concept_id": 19, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_13", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Model?", "ground_truth": ["Machine Learning"], "concept_id": 13, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_171", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Normalization?", "ground_truth": ["Data Preprocessing"], "concept_id": 171, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_60", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Hard Margin SVM?", "ground_truth": ["Support Vector Machine", "Margin"], "concept_id": 60, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_76", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for K-Means++ Initialization?", "ground_truth": ["K-Means Initialization"], "concept_id": 76, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_198", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Grid Search?", "ground_truth": ["Hyperparameter Tuning"], "concept_id": 198, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_61", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Soft Margin SVM?", "ground_truth": ["Support Vector Machine", "Margin"], "concept_id": 61, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_27", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Decision Tree?", "ground_truth": ["Supervised Learning", "Algorithm"], "concept_id": 27, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_99", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Mean Squared Error?", "ground_truth": ["Loss Function", "Regression"], "concept_id": 99, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_73", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Cluster Update?", "ground_truth": ["K-Means Clustering", "Centroid"], "concept_id": 73, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_118", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Filter?", "ground_truth": ["Convolution Operation"], "concept_id": 118, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_164", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Recall?", "ground_truth": ["True Positive", "False Negative"], "concept_id": 164, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_95", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Stochastic Gradient Descent?", "ground_truth": ["Gradient Descent"], "concept_id": 95, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_43", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Log-Loss?", "ground_truth": ["Loss Function", "Logistic Regression"], "concept_id": 43, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_96", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Mini-Batch Gradient Descent?", "ground_truth": ["Gradient Descent"], "concept_id": 96, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_92", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Forward Propagation?", "ground_truth": ["Neural Network", "Artificial Neuron"], "concept_id": 92, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_55", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Support Vector Machine?", "ground_truth": ["Supervised Learning", "Algorithm", "Classification"], "concept_id": 55, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_70", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for K-Means Clustering?", "ground_truth": ["Unsupervised Learning", "Algorithm"], "concept_id": 70, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_167", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for AUC?", "ground_truth": ["ROC Curve"], "concept_id": 167, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_20", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for K Selection?", "ground_truth": ["Hyperparameter", "K-Nearest Neighbors"], "concept_id": 20, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_157", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Confusion Matrix?", "ground_truth": ["Classification"], "concept_id": 157, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_178", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Feature Selection?", "ground_truth": ["Feature Engineering"], "concept_id": 178, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_45", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Multiclass Classification?", "ground_truth": ["Classification"], "concept_id": 45, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_138", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Inception?", "ground_truth": ["CNN Architecture"], "concept_id": 138, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_64", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Linear Kernel?", "ground_truth": ["Kernel Trick"], "concept_id": 64, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_176", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Label Encoding?", "ground_truth": ["Data Preprocessing", "Label"], "concept_id": 176, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_120", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Stride?", "ground_truth": ["Convolution Operation"], "concept_id": 120, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_181", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Computational Complexity?", "ground_truth": ["Algorithm"], "concept_id": 181, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_71", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Centroid?", "ground_truth": ["K-Means Clustering"], "concept_id": 71, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_144", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for ImageNet?", "ground_truth": ["Convolutional Neural Network", "Pre-Trained Model"], "concept_id": 144, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_58", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Support Vectors?", "ground_truth": ["Support Vector Machine", "Margin"], "concept_id": 58, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_85", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Activation Function?", "ground_truth": ["Artificial Neuron"], "concept_id": 85, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_16", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for K-Nearest Neighbors?", "ground_truth": ["Supervised Learning", "Algorithm"], "concept_id": 16, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_186", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Online Learning?", "ground_truth": ["Machine Learning", "Training Data"], "concept_id": 186, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_10", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Label?", "ground_truth": ["Supervised Learning"], "concept_id": 10, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_82", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Neural Network?", "ground_truth": ["Supervised Learning", "Algorithm"], "concept_id": 82, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_104", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Exploding Gradient?", "ground_truth": ["Backpropagation", "Gradient Descent"], "concept_id": 104, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_172", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Standardization?", "ground_truth": ["Data Preprocessing"], "concept_id": 172, "hop_depth": 1}
{"id": "machine-learning-textbook_T2_18", "domain": "machine-learning-textbook", "type": "T2_dependency", "query": "What are the prerequisites for Euclidean Distance?", "ground_truth": ["Distance Metric"], "concept_id": 18, "hop_depth": 1}
{"id": "machine-learning-textbook_T3_1_136", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to VGG?", "ground_truth": ["VGG", "CNN Architecture", "Convolutional Neural Network", "Algorithm", "Machine Learning"], "concept_id": 136, "hop_depth": 4, "path_ids": [136, 133, 116, 14, 1]}
{"id": "machine-learning-textbook_T3_1_62", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Slack Variables?", "ground_truth": ["Slack Variables", "Soft Margin SVM", "Support Vector Machine", "Supervised Learning", "Machine Learning"], "concept_id": 62, "hop_depth": 4, "path_ids": [62, 61, 55, 2, 1]}
{"id": "machine-learning-textbook_T3_1_101", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Epoch?", "ground_truth": ["Epoch", "Training Data", "Machine Learning"], "concept_id": 101, "hop_depth": 2, "path_ids": [101, 6, 1]}
{"id": "machine-learning-textbook_T3_1_46", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Maximum Likelihood?", "ground_truth": ["Maximum Likelihood", "Logistic Regression", "Supervised Learning", "Machine Learning"], "concept_id": 46, "hop_depth": 3, "path_ids": [46, 41, 2, 1]}
{"id": "machine-learning-textbook_T3_1_44", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Binary Classification?", "ground_truth": ["Binary Classification", "Classification", "Supervised Learning", "Machine Learning"], "concept_id": 44, "hop_depth": 3, "path_ids": [44, 4, 2, 1]}
{"id": "machine-learning-textbook_T3_1_178", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Feature Selection?", "ground_truth": ["Feature Selection", "Feature Engineering", "Feature", "Machine Learning"], "concept_id": 178, "hop_depth": 3, "path_ids": [178, 177, 9, 1]}
{"id": "machine-learning-textbook_T3_1_178", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Feature Selection?", "ground_truth": ["Feature Selection", "Feature Engineering", "Feature", "Machine Learning"], "concept_id": 178, "hop_depth": 3, "path_ids": [178, 177, 9, 1]}
{"id": "machine-learning-textbook_T3_1_138", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Inception?", "ground_truth": ["Inception", "CNN Architecture", "Convolutional Neural Network", "Algorithm", "Machine Learning"], "concept_id": 138, "hop_depth": 4, "path_ids": [138, 133, 116, 14, 1]}
{"id": "machine-learning-textbook_T3_1_95", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Stochastic Gradient Descent?", "ground_truth": ["Stochastic Gradient Descent", "Gradient Descent", "Backpropagation", "Neural Network", "Supervised Learning", "Machine Learning"], "concept_id": 95, "hop_depth": 5, "path_ids": [95, 94, 93, 82, 2, 1]}
{"id": "machine-learning-textbook_T3_1_44", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Binary Classification?", "ground_truth": ["Binary Classification", "Classification", "Supervised Learning", "Machine Learning"], "concept_id": 44, "hop_depth": 3, "path_ids": [44, 4, 2, 1]}
{"id": "machine-learning-textbook_T3_1_34", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Pruning?", "ground_truth": ["Pruning", "Decision Tree", "Supervised Learning", "Machine Learning"], "concept_id": 34, "hop_depth": 3, "path_ids": [34, 27, 2, 1]}
{"id": "machine-learning-textbook_T3_1_199", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Random Search?", "ground_truth": ["Random Search", "Hyperparameter Tuning", "Hyperparameter", "Model", "Machine Learning"], "concept_id": 199, "hop_depth": 4, "path_ids": [199, 197, 15, 13, 1]}
{"id": "machine-learning-textbook_T3_1_47", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to One-vs-All?", "ground_truth": ["One-vs-All", "Logistic Regression", "Supervised Learning", "Machine Learning"], "concept_id": 47, "hop_depth": 3, "path_ids": [47, 41, 2, 1]}
{"id": "machine-learning-textbook_T3_1_185", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Batch Processing?", "ground_truth": ["Batch Processing", "Training Data", "Machine Learning"], "concept_id": 185, "hop_depth": 2, "path_ids": [185, 6, 1]}
{"id": "machine-learning-textbook_T3_1_143", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Domain Adaptation?", "ground_truth": ["Domain Adaptation", "Transfer Learning", "Algorithm", "Machine Learning"], "concept_id": 143, "hop_depth": 3, "path_ids": [143, 139, 14, 1]}
{"id": "machine-learning-textbook_T3_1_100", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Cross-Entropy Loss?", "ground_truth": ["Cross-Entropy Loss", "Loss Function", "Model", "Machine Learning"], "concept_id": 100, "hop_depth": 3, "path_ids": [100, 98, 13, 1]}
{"id": "machine-learning-textbook_T3_1_143", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Domain Adaptation?", "ground_truth": ["Domain Adaptation", "Transfer Learning", "Algorithm", "Machine Learning"], "concept_id": 143, "hop_depth": 3, "path_ids": [143, 139, 14, 1]}
{"id": "machine-learning-textbook_T3_1_130", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Translation Invariance?", "ground_truth": ["Translation Invariance", "Convolutional Neural Network", "Algorithm", "Machine Learning"], "concept_id": 130, "hop_depth": 3, "path_ids": [130, 116, 14, 1]}
{"id": "machine-learning-textbook_T3_1_134", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to LeNet?", "ground_truth": ["LeNet", "CNN Architecture", "Convolutional Neural Network", "Algorithm", "Machine Learning"], "concept_id": 134, "hop_depth": 4, "path_ids": [134, 133, 116, 14, 1]}
{"id": "machine-learning-textbook_T3_1_182", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Time Complexity?", "ground_truth": ["Time Complexity", "Computational Complexity", "Algorithm", "Machine Learning"], "concept_id": 182, "hop_depth": 3, "path_ids": [182, 181, 14, 1]}
{"id": "machine-learning-textbook_T3_1_40", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Feature Space Partitioning?", "ground_truth": ["Feature Space Partitioning", "Feature", "Machine Learning"], "concept_id": 40, "hop_depth": 2, "path_ids": [40, 9, 1]}
{"id": "machine-learning-textbook_T3_1_111", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Input Layer?", "ground_truth": ["Input Layer", "Neural Network", "Supervised Learning", "Machine Learning"], "concept_id": 111, "hop_depth": 3, "path_ids": [111, 82, 2, 1]}
{"id": "machine-learning-textbook_T3_1_115", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Universal Approximation?", "ground_truth": ["Universal Approximation", "Neural Network", "Supervised Learning", "Machine Learning"], "concept_id": 115, "hop_depth": 3, "path_ids": [115, 82, 2, 1]}
{"id": "machine-learning-textbook_T3_1_174", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Z-Score Normalization?", "ground_truth": ["Z-Score Normalization", "Standardization", "Data Preprocessing", "Training Data", "Machine Learning"], "concept_id": 174, "hop_depth": 4, "path_ids": [174, 172, 170, 6, 1]}
{"id": "machine-learning-textbook_T3_1_127", "domain": "machine-learning-textbook", "type": "T3_path", "query": "What is the prerequisite chain from Machine Learning to Max Pooling?", "ground_truth": ["Max Pooling", "Pooling Layer", "Convolutional Neural Network", "Algorithm", "Machine Learning"], "concept_id": 127, "hop_depth": 4, "path_ids": [127, 126, 116, 14, 1]}
{"id": "machine-learning-textbook_T4_FOUND", "domain": "machine-learning-textbook", "type": "T4_aggregate", "query": "List all FOUND concepts in this knowledge graph", "ground_truth": ["Machine Learning", "Supervised Learning", "Unsupervised Learning", "Classification", "Regression", "Training Data", "Test Data", "Validation Data", "Feature", "Label", "Instance", "Feature Vector", "Model", "Algorithm", "Hyperparameter", "Categorical Features", "Continuous Features", "Feature Space Partitioning", "Feature Map", "Pre-Trained Model", "Feature Extraction", "Model Zoo", "Validation Error", "Cross-Validation", "K-Fold Cross-Validation", "Label Encoding", "Feature Engineering", "Feature Selection", "Model Evaluation", "Model Selection", "Hyperparameter Tuning"], "taxonomy_id": "FOUND", "hop_depth": 0}
{"id": "machine-learning-textbook_T4_KNN", "domain": "machine-learning-textbook", "type": "T4_aggregate", "query": "List all KNN concepts in this knowledge graph", "ground_truth": ["K-Nearest Neighbors", "Distance Metric", "Euclidean Distance", "Manhattan Distance", "K Selection", "Decision Boundary", "Voronoi Diagram", "Curse of Dimensionality", "KNN for Classification", "KNN for Regression", "Lazy Learning"], "taxonomy_id": "KNN", "hop_depth": 0}
{"id": "machine-learning-textbook_T4_TREE", "domain": "machine-learning-textbook", "type": "T4_aggregate", "query": "List all TREE concepts in this knowledge graph", "ground_truth": ["Decision Tree", "Tree Node", "Leaf Node", "Splitting Criterion", "Entropy", "Information Gain", "Gini Impurity", "Pruning", "Overfitting", "Underfitting", "Tree Depth", "Cross-Entropy Loss"], "taxonomy_id": "TREE", "hop_depth": 0}
{"id": "machine-learning-textbook_T4_MISC", "domain": "machine-learning-textbook", "type": "T4_aggregate", "query": "List all MISC concepts in this knowledge graph", "ground_truth": ["Logistic Regression"], "taxonomy_id": "MISC", "hop_depth": 0}
{"id": "machine-learning-textbook_T4_LOGREG", "domain": "machine-learning-textbook", "type": "T4_aggregate", "query": "List all LOGREG concepts in this knowledge graph", "ground_truth": ["Sigmoid Function", "Log-Loss", "Binary Classification", "Multiclass Classification", "Maximum Likelihood", "One-vs-All", "One-vs-One", "Softmax Function", "Sigmoid Activation"], "taxonomy_id": "LOGREG", "hop_depth": 0}
{"id": "machine-learning-textbook_T4_REG", "domain": "machine-learning-textbook", "type": "T4_aggregate", "query": "List all REG concepts in this knowledge graph", "ground_truth": ["Regularization", "L1 Regularization", "L2 Regularization", "Ridge Regression", "Lasso Regression"], "taxonomy_id": "REG", "hop_depth": 0}
{"id": "machine-learning-textbook_T4_SVM", "domain": "machine-learning-textbook", "type": "T4_aggregate", "query": "List all SVM concepts in this knowledge graph", "ground_truth": ["Support Vector Machine", "Hyperplane", "Margin", "Support Vectors", "Margin Maximization", "Hard Margin SVM", "Soft Margin SVM", "Slack Variables", "Kernel Trick", "Linear Kernel", "Polynomial Kernel", "Radial Basis Function", "Gaussian Kernel", "Dual Formulation", "Primal Formulation", "Kernel Size"], "taxonomy_id": "SVM", "hop_depth": 0}
{"id": "machine-learning-textbook_T4_CLUST", "domain": "machine-learning-textbook", "type": "T4_aggregate", "query": "List all CLUST concepts in this knowledge graph", "ground_truth": ["K-Means Clustering", "Centroid", "Cluster Assignment", "Cluster Update", "K-Means Initialization", "Random Initialization", "K-Means++ Initialization", "Elbow Method", "Silhouette Score", "Within-Cluster Variance", "Convergence Criteria", "Inertia"], "taxonomy_id": "CLUST", "hop_depth": 0}
{"id": "machine-learning-textbook_T4_NN", "domain": "machine-learning-textbook", "type": "T4_aggregate", "query": "List all NN concepts in this knowledge graph", "ground_truth": ["Neural Network", "Artificial Neuron", "Perceptron", "Activation Function", "ReLU", "Tanh", "Leaky ReLU", "Weights", "Bias", "Forward Propagation", "Backpropagation", "Gradient Descent", "Stochastic Gradient Descent", "Mini-Batch Gradient Descent", "Learning Rate", "Loss Function", "Mean Squared Error", "Epoch", "Batch Size", "Vanishing Gradient", "Exploding Gradient", "Weight Initialization", "Xavier Initialization", "He Initialization", "Fully Connected Layer", "Hidden Layer", "Output Layer", "Input Layer", "Network Architecture", "Deep Learning", "Multilayer Perceptron", "Universal Approximation", "Pooling Layer", "Freezing Layers", "Learning Rate Scheduling", "Bias-Variance Tradeoff", "Batch Processing"], "taxonomy_id": "NN", "hop_depth": 0}
{"id": "machine-learning-textbook_T4_CNN", "domain": "machine-learning-textbook", "type": "T4_aggregate", "query": "List all CNN concepts in this knowledge graph", "ground_truth": ["Convolutional Neural Network", "Convolution Operation", "Filter", "Stride", "Padding", "Valid Padding", "Same Padding", "Receptive Field", "Max Pooling", "Average Pooling", "Spatial Hierarchies", "Translation Invariance", "Local Connectivity", "Weight Sharing", "CNN Architecture", "LeNet", "AlexNet", "VGG", "ResNet", "Inception"], "taxonomy_id": "CNN", "hop_depth": 0}
{"id": "machine-learning-textbook_T4_TL", "domain": "machine-learning-textbook", "type": "T4_aggregate", "query": "List all TL concepts in this knowledge graph", "ground_truth": ["Transfer Learning", "Fine-Tuning", "Domain Adaptation", "ImageNet"], "taxonomy_id": "TL", "hop_depth": 0}
{"id": "machine-learning-textbook_T4_EVAL", "domain": "machine-learning-textbook", "type": "T4_aggregate", "query": "List all EVAL concepts in this knowledge graph", "ground_truth": ["Training Error", "Test Error", "Generalization", "Stratified Sampling", "Holdout Method", "Confusion Matrix", "True Positive", "False Positive", "True Negative", "False Negative", "Accuracy", "Precision", "Recall", "F1 Score", "ROC Curve", "AUC", "Sensitivity", "Specificity", "Data Preprocessing"], "taxonomy_id": "EVAL", "hop_depth": 0}
{"id": "machine-learning-textbook_T4_PREP", "domain": "machine-learning-textbook", "type": "T4_aggregate", "query": "List all PREP concepts in this knowledge graph", "ground_truth": ["Normalization", "Standardization", "Min-Max Scaling", "Z-Score Normalization", "One-Hot Encoding", "Dimensionality Reduction", "Data Augmentation"], "taxonomy_id": "PREP", "hop_depth": 0}
{"id": "machine-learning-textbook_T4_OPT", "domain": "machine-learning-textbook", "type": "T4_aggregate", "query": "List all OPT concepts in this knowledge graph", "ground_truth": ["Computational Complexity", "Time Complexity", "Space Complexity", "Scalability", "Online Learning", "Optimizer", "Adam Optimizer", "RMSprop", "Momentum", "Nesterov Momentum", "Gradient Clipping", "Dropout", "Early Stopping", "Grid Search", "Random Search", "Bayesian Optimization"], "taxonomy_id": "OPT", "hop_depth": 0}
{"id": "machine-learning-textbook_T5_65_63", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Polynomial Kernel relate to Kernel Trick?", "ground_truth": ["Polynomial Kernel", "Kernel Trick"], "concept_id_a": 65, "concept_id_b": 63, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_180_170", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Data Augmentation relate to Data Preprocessing?", "ground_truth": ["Data Augmentation", "Data Preprocessing"], "concept_id_a": 180, "concept_id_b": 170, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_41_2", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Logistic Regression relate to Supervised Learning?", "ground_truth": ["Logistic Regression", "Supervised Learning"], "concept_id_a": 41, "concept_id_b": 2, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_107_105", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does He Initialization relate to Weight Initialization?", "ground_truth": ["He Initialization", "Weight Initialization"], "concept_id_a": 107, "concept_id_b": 105, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_72_71", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Cluster Assignment relate to Centroid?", "ground_truth": ["Cluster Assignment", "Centroid", "K-Means Clustering"], "concept_id_a": 72, "concept_id_b": 71, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_56_55", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Hyperplane relate to Support Vector Machine?", "ground_truth": ["Hyperplane", "Support Vector Machine"], "concept_id_a": 56, "concept_id_b": 55, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_134_133", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does LeNet relate to CNN Architecture?", "ground_truth": ["LeNet", "CNN Architecture"], "concept_id_a": 134, "concept_id_b": 133, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_58_57", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Support Vectors relate to Margin?", "ground_truth": ["Support Vectors", "Margin", "Support Vector Machine"], "concept_id_a": 58, "concept_id_b": 57, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_193_50", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Dropout relate to Regularization?", "ground_truth": ["Dropout", "Regularization"], "concept_id_a": 193, "concept_id_b": 50, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_189_187", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does RMSprop relate to Optimizer?", "ground_truth": ["RMSprop", "Optimizer"], "concept_id_a": 189, "concept_id_b": 187, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_2_1", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Supervised Learning relate to Machine Learning?", "ground_truth": ["Supervised Learning", "Machine Learning"], "concept_id_a": 2, "concept_id_b": 1, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_116_113", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Convolutional Neural Network relate to Deep Learning?", "ground_truth": ["Convolutional Neural Network", "Deep Learning"], "concept_id_a": 116, "concept_id_b": 113, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_175_170", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does One-Hot Encoding relate to Data Preprocessing?", "ground_truth": ["One-Hot Encoding", "Data Preprocessing"], "concept_id_a": 175, "concept_id_b": 170, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_11_9", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Instance relate to Feature?", "ground_truth": ["Instance", "Feature"], "concept_id_a": 11, "concept_id_b": 9, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_42_41", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Sigmoid Function relate to Logistic Regression?", "ground_truth": ["Sigmoid Function", "Logistic Regression"], "concept_id_a": 42, "concept_id_b": 41, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_130_117", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Translation Invariance relate to Convolution Operation?", "ground_truth": ["Translation Invariance", "Convolution Operation", "Convolutional Neural Network"], "concept_id_a": 130, "concept_id_b": 117, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_110_108", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Output Layer relate to Fully Connected Layer?", "ground_truth": ["Output Layer", "Fully Connected Layer", "Neural Network"], "concept_id_a": 110, "concept_id_b": 108, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_88_85", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Sigmoid Activation relate to Activation Function?", "ground_truth": ["Sigmoid Activation", "Activation Function"], "concept_id_a": 88, "concept_id_b": 85, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_24_4", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does KNN for Classification relate to Classification?", "ground_truth": ["KNN for Classification", "Classification"], "concept_id_a": 24, "concept_id_b": 4, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_89_85", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Leaky ReLU relate to Activation Function?", "ground_truth": ["Leaky ReLU", "Activation Function"], "concept_id_a": 89, "concept_id_b": 85, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_32_31", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Information Gain relate to Entropy?", "ground_truth": ["Information Gain", "Entropy"], "concept_id_a": 32, "concept_id_b": 31, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_34_35", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Pruning relate to Overfitting?", "ground_truth": ["Pruning", "Overfitting"], "concept_id_a": 34, "concept_id_b": 35, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_173_171", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Min-Max Scaling relate to Normalization?", "ground_truth": ["Min-Max Scaling", "Normalization"], "concept_id_a": 173, "concept_id_b": 171, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_28_27", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Tree Node relate to Decision Tree?", "ground_truth": ["Tree Node", "Decision Tree"], "concept_id_a": 28, "concept_id_b": 27, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_190_94", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Momentum relate to Gradient Descent?", "ground_truth": ["Momentum", "Gradient Descent"], "concept_id_a": 190, "concept_id_b": 94, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_47_41", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does One-vs-All relate to Logistic Regression?", "ground_truth": ["One-vs-All", "Logistic Regression"], "concept_id_a": 47, "concept_id_b": 41, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_48_45", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does One-vs-One relate to Multiclass Classification?", "ground_truth": ["One-vs-One", "Multiclass Classification"], "concept_id_a": 48, "concept_id_b": 45, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_169_160", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Specificity relate to True Negative?", "ground_truth": ["Specificity", "True Negative"], "concept_id_a": 169, "concept_id_b": 160, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_195_7", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Model Evaluation relate to Test Data?", "ground_truth": ["Model Evaluation", "Test Data"], "concept_id_a": 195, "concept_id_b": 7, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_60_55", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Hard Margin SVM relate to Support Vector Machine?", "ground_truth": ["Hard Margin SVM", "Support Vector Machine"], "concept_id_a": 60, "concept_id_b": 55, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_97_15", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Learning Rate relate to Hyperparameter?", "ground_truth": ["Learning Rate", "Hyperparameter"], "concept_id_a": 97, "concept_id_b": 15, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_188_187", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Adam Optimizer relate to Optimizer?", "ground_truth": ["Adam Optimizer", "Optimizer"], "concept_id_a": 188, "concept_id_b": 187, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_151_7", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Test Error relate to Test Data?", "ground_truth": ["Test Error", "Test Data"], "concept_id_a": 151, "concept_id_b": 7, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_77_15", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Elbow Method relate to Hyperparameter?", "ground_truth": ["Elbow Method", "Hyperparameter"], "concept_id_a": 77, "concept_id_b": 15, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_73_70", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Cluster Update relate to K-Means Clustering?", "ground_truth": ["Cluster Update", "K-Means Clustering"], "concept_id_a": 73, "concept_id_b": 70, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_111_108", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Input Layer relate to Fully Connected Layer?", "ground_truth": ["Input Layer", "Fully Connected Layer", "Neural Network"], "concept_id_a": 111, "concept_id_b": 108, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_144_116", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does ImageNet relate to Convolutional Neural Network?", "ground_truth": ["ImageNet", "Convolutional Neural Network"], "concept_id_a": 144, "concept_id_b": 116, "hop_depth": 1}
{"id": "machine-learning-textbook_T5_155_153", "domain": "machine-learning-textbook", "type": "T5_cross_concept", "query": "How does Stratified Sampling relate to Cross-Validation?", "ground_truth": ["Stratified Sampling", "Cross-Validation"], "concept_id_a": 155, "concept_id_b": 153, "hop_depth": 1}