Each record in the database describes a Boston suburb or town. The data was drawn from the Boston Standard Metropolitan Statistical Area (SMSA) in 1970. The attributes are defined as follows (taken from the UCI Machine Learning Repository1):
CRIM: per capita crime rate by town
ZN: proportion of residential land zoned for lots over 25,000 sq.ft.
INDUS: proportion of non-retail business acres per town
CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
NOX: nitric oxides concentration (parts per 10 million)
1https://archive.ics.uci.edu/ml/datasets/Housing 123 20.2. Load the Dataset 124
RM: average number of rooms per dwelling
AGE: proportion of owner-occupied units built prior to 1940
DIS: weighted distances to five Boston employment centers
RAD: index of accessibility to radial highways
TAX: full-value property-tax rate per $10,000
PTRATIO: pupil-teacher ratio by town 12. B: 1000(Bk−0.63)2 where Bk is the proportion of blacks by town 13.
LSTAT: % lower status of the population
MEDV: Median value of owner-occupied homes in $1000s
We can see that the input attributes have a mixture of units.
It is an implementation of the XGBoost algorithm specifically designed for regression tasks in machine learning.
It's an ensemble learning method that combines the predictions of multiple decision trees to improve accuracy and generalization. XGBoost is known for its efficiency, speed, and performance, making it a popular choice for regression problems.
It utilizes gradient boosting, where weak learners (decision trees) are built sequentially, each correcting the errors of the previous one. Parameters like learning rate, number of trees, and tree-specific settings can be tuned to optimize performance for a given regression problem.