Machine learning is a powerful tool that allows us to make predictions and analyze data using a variety of algorithms. In this article, we will focus on using the XGBoost algorithm for regression tasks in R. We will be using the Boston Housing Price dataset from the UCI repository, and the CARET package to train and evaluate our models.
The first step in using XGBoost for regression is to load our dataset. We can do this using the read.csv function, which allows us to import a CSV file into R. The Boston Housing Price dataset contains information on different properties in the Boston area, including the median value of homes in thousands of dollars.
Once we have loaded our dataset, we need to split it into training and testing sets. This is important because we want to use the training set to train our model, and the testing set to evaluate its performance. We can use the createDataPartition function from the CARET package to do this.
Next, we will use the xgboost function from the XGBoost package to train our model. This function takes several parameters, including the training set, the target variable, and the number of trees to be used in the model. We can also use the caret package to tune our model by using Grid Search Cross Validation (GSCV).
After training our model, we can use it to make predictions on the testing set. We can use the predict function to do this. The predict function takes the model and the testing set as input, and returns the predicted values for the target variable.
Finally, we can evaluate the performance of our model by comparing the predicted values with the actual values from the testing set. We can use the R-squared metric to evaluate the goodness of fit of our model. A high R-squared value indicates that the model fits the data well.
In conclusion, XGBoost is a powerful algorithm for regression tasks in R. It is easy to use, fast and efficient, and can be tuned using GSCV. The Boston Housing Price dataset from UCI is a good choice for beginners to practice on, and can be easily imported into R using the CARET package.
In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming:
Machine Learning in R | Data Science for Beginners | XGBoost | Regression | Boston Dataset | CARET.
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- Machine Learning in R | Data Science for Beginners | XGBoost | Regression | Boston Dataset | CARET.
- Practical Data Science tutorials with Python and R for Beginners and Citizen Data Scientists.
- Practical Machine Learning tutorials with Python and R for Beginners and Machine Learning Developers.
Machine Learning in R | Data Science for Beginners | XGBoost | Regression | Boston Dataset | CARET:
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
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