Journal: PLOS ONE

Article Title: Comparing machine learning methods for predicting land development intensity

doi: 10.1371/journal.pone.0282476

Figure Lengend Snippet: Parameter comparison of the maximum prediction.

Article Snippet: The XGBOOST algorithm has proven its effectiveness in numerous machine learning and data mining challenges for prediction and classification problems and was rated as the top solution algorithm in a machine learning competition held on the Kaggle website [ ], with the main advantages being the minimum requirement for attribute normalization, intelligent handling of missing values, and providing solutions that avoid overfitting [ , ].

Techniques: Comparison, Plasmid Preparation

Journal: PLOS ONE

Article Title: Comparing machine learning methods for predicting land development intensity

doi: 10.1371/journal.pone.0282476

Figure Lengend Snippet: Modeling flowchart of XGBOOST based on land development intensity.

Article Snippet: The XGBOOST algorithm has proven its effectiveness in numerous machine learning and data mining challenges for prediction and classification problems and was rated as the top solution algorithm in a machine learning competition held on the Kaggle website [ ], with the main advantages being the minimum requirement for attribute normalization, intelligent handling of missing values, and providing solutions that avoid overfitting [ , ].

Techniques:

Journal: PLOS ONE

Article Title: Comparing machine learning methods for predicting land development intensity

doi: 10.1371/journal.pone.0282476

Figure Lengend Snippet: XGBOOST model parameters in the process of training the learning curve.

Article Snippet: The XGBOOST algorithm has proven its effectiveness in numerous machine learning and data mining challenges for prediction and classification problems and was rated as the top solution algorithm in a machine learning competition held on the Kaggle website [ ], with the main advantages being the minimum requirement for attribute normalization, intelligent handling of missing values, and providing solutions that avoid overfitting [ , ].

Techniques:

Journal: PLOS ONE

Article Title: Comparing machine learning methods for predicting land development intensity

doi: 10.1371/journal.pone.0282476

Figure Lengend Snippet: XGBOOST super parameter combination of the model.

Article Snippet: The XGBOOST algorithm has proven its effectiveness in numerous machine learning and data mining challenges for prediction and classification problems and was rated as the top solution algorithm in a machine learning competition held on the Kaggle website [ ], with the main advantages being the minimum requirement for attribute normalization, intelligent handling of missing values, and providing solutions that avoid overfitting [ , ].

Techniques:

Journal: PLOS ONE

Article Title: Comparing machine learning methods for predicting land development intensity

doi: 10.1371/journal.pone.0282476

Figure Lengend Snippet: XGBOOST model test set verification results description.

Article Snippet: The XGBOOST algorithm has proven its effectiveness in numerous machine learning and data mining challenges for prediction and classification problems and was rated as the top solution algorithm in a machine learning competition held on the Kaggle website [ ], with the main advantages being the minimum requirement for attribute normalization, intelligent handling of missing values, and providing solutions that avoid overfitting [ , ].

Techniques:

Journal: PLOS ONE

Article Title: Comparing machine learning methods for predicting land development intensity

doi: 10.1371/journal.pone.0282476

Figure Lengend Snippet: The fitting map of land development intensity test based on the XGBOOST model.

Article Snippet: The XGBOOST algorithm has proven its effectiveness in numerous machine learning and data mining challenges for prediction and classification problems and was rated as the top solution algorithm in a machine learning competition held on the Kaggle website [ ], with the main advantages being the minimum requirement for attribute normalization, intelligent handling of missing values, and providing solutions that avoid overfitting [ , ].

Techniques: