code: def algo_to_modelname(algo):
      if algo == "aggregator":
          return "H2O Aggregator Model"
      if algo == "deeplearning":
          return "Deep Learning - Neural Network"
      if algo == "xgboost":
          return "XGBoost"
      if algo == "drf":
          return "Random Forest Model in H2O"
      if algo == "dt":
          return "Decision Tree Model in H2O"
      if algo == "upliftdrf":
          return "Uplift Random Forest Model in H2O"
      if algo == "gbm":
          return "Gradient Boosting Machine"
      if algo == "glm":
          return "H2O Generalized Linear Models"
      if algo == "glrm":
          return "Generalized Low Rank Model"
      if algo == "kmeans":
          return "KMeans Model in H2O"
      if algo == "naivebayes":
          return "Naive Bayes Model in H2O"
      if algo == "pca":
          return "Principal Components Analysis"
      if algo == "svd":
          return "Singular Value Decomposition"
      if algo == "stackedensemble":
          return "H2O Stacked Ensemble"
      if algo == "psvm":
          return "Support Vector Machine"
      if algo == "anovaglm":
          return "ANOVA GLM"
      if algo == "targetencoder":
          return "Target Encoder"
      if algo == "gam":
          return "Generalized Additive Model"
      if algo == "modelselection":
          return "Model Selection"
      if algo == "infogram":
          return "Infogram"
      if algo == "adaboost":
          return "AdaBoost Model"
      return algo
  
file_path: h2o-3/h2o-bindings/bin/gen_R.py
imports: from collections import OrderedDict
  from copy import deepcopy
  from functools import partial
  import bindings
  from custom import get_customizations_for, reformat_block
label_desc: Use image node_id 4 for calling a global function with example usage: algo_to_modelname(algo) and returns: algo, str, str, str, str, str, str, str, str, str, str, str, str, str, str, str, str, str, str, str, str, str