Gradient Boosting Machine (GBM) is a powerful machine learning algorithm that is used for both classification and regression tasks. It is a type of ensemble learning method, which means it combines multiple weak models to create a strong model. GBM is a popular algorithm for data science and machine learning projects because it is known for its high accuracy and ability to handle large datasets.
The Boston Housing Price dataset from UCI is a popular dataset for machine learning and data science. It contains information about various properties in Boston and includes a variety of features such as the crime rate, the number of rooms, and the median value of owner-occupied homes. The goal of this dataset is to predict the median value of a property based on the other features.
To use GBM with the Boston Housing Price dataset, you would first need to load the dataset into Python. This can be done using a library such as pandas. Once the dataset is loaded, you would then need to split it into training and testing sets. The training set is used to train the GBM model, and the testing set is used to evaluate its performance.
Next, you would need to define the GBM model using a library such as xgboost or LightGBM. These libraries provide a wide range of tools for building GBM models in Python. You would then need to train the model using the training set and evaluate its performance using the testing set.
The GBM algorithm works by creating multiple decision trees, and then boosting them to create a strong model. Each decision tree is built using a random subset of the features and a random subset of the training data. The final prediction is made by combining the predictions of all the decision trees.
One of the key advantages of GBM is that it can handle missing values and outliers. The algorithm can handle missing values and outliers by building multiple decision trees and combining the predictions.
Another advantage of GBM is that it is less prone to overfitting than other algorithms. Overfitting occurs when a model is too complex and performs well on the training data but not on the testing data. GBM reduces overfitting by building multiple decision trees and combining the predictions.
In conclusion, GBM is a powerful machine learning algorithm that can be used for both classification and regression tasks. The Boston Housing Price dataset from UCI is a popular dataset for machine learning and data science that can be used with GBM to predict the median value of a property based on the other features. GBM can handle missing values and outliers and is less prone to overfitting than other algorithms.
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 and Data Science in Python using GB with Boston House Price Dataset | Pandas.
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.
Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners
Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:
All Notebooks in One Bundle: Data Science Recipes and Examples in Python & R.
End-to-End Python Machine Learning Recipes & Examples.
End-to-End R Machine Learning Recipes & Examples.
Applied Statistics with R for Beginners and Business Professionals
Data Science and Machine Learning Projects in Python: Tabular Data Analytics
Data Science and Machine Learning Projects in R: Tabular Data Analytics
Python Machine Learning & Data Science Recipes: Learn by Coding
R Machine Learning & Data Science Recipes: Learn by Coding
Comparing Different Machine Learning Algorithms in Python for Classification (FREE)
There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $29.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.