Data Science Coding | H2O in Python with Grid Search Cross Validation | IRIS Dataset
H2O.ai is an open-source platform that provides a wide range of machine learning algorithms and tools for building, deploying, and managing models. It is written in Java and has APIs for several programming languages, including Python.
Grid Search Cross Validation is a method used to find the best set of parameters for a machine learning model. It works by testing different combinations of parameters and evaluating their performance. The best combination of parameters is then chosen for the final model.
In this example, we will be using the IRIS dataset, which contains information about different variations of the IRIS flower, such as the setosa, virginica, and versicolor. We will use H2O.ai in Python to build a machine learning model and use Grid Search Cross Validation to find the best set of parameters for the model.
First, we will preprocess the IRIS data and split it into training and testing sets. Next, we will use H2O.ai to define the machine learning model, such as a Random Forest or a Gradient Boosting model. We will then use Grid Search Cross Validation to test different combinations of parameters, such as the number of trees in the forest or the depth of each tree, and evaluate their performance.
After the model is trained and the best set of parameters is found, we can use it to classify new IRIS flowers based on their characteristics.
In summary, H2O.ai in Python with Grid Search Cross Validation using IRIS Dataset is a powerful combination of an open-source platform, programming language and techniques that can be used to classify the IRIS flowers. H2O.ai provides a wide range of machine learning algorithms and tools and Grid Search Cross Validation is used to fine-tune the best set of parameters for the model.
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 Python programming: H2O in Python with Grid Search Cross Validation.
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.
Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R: