ml_learning.graphviz

digraph G {
    
    subgraph cluster_gc {
        label = "general concepts";
        training_vs_testing -> overfitting_and_verification;
        regularization
    }
    
    linear_regression -> persception_learning;
    logistic_regression -> linear_regression;
    regularization -> logistic_regression;
    NN -> linear_regression;
    
    subgraph cluster_nn {
        label = "neural network"
        
        CNN -> NN;
        RNN -> NN;
        word_embeding -> RNN;
        attention -> RNN;
        GRU -> RNN;
        LSTM -> RNN;
        GAN -> auto_encoder -> NN;
        
    }
    
    subgraph cluster_dr {
        label = "dimensionality reduction";
        auto_encoder
        PCA;
        word_embedding;
    }
    
    SVM -> logistic_regression;
    
    subgraph cluster_clustering {
        label = "clustering";
        K_means;
    }
    
    subgraph cluster_blending {
        label = "blending and boosting";
        adaptive_boosting -> blending_bagging;
    }
    
    subgraph cluster_dt {
        label = "decision tree";
        GBDT -> adaptive_boosting;
        GBDT -> random_forest -> decision_tree;
    }
}