How to install, load and describe Penn Machine Learning Benchmarks – Yeast Datasets

The Penn Machine Learning Benchmarks (PMLB) is a collection of datasets for evaluating machine learning algorithms. One of the datasets included in PMLB is the Yeast dataset, which consists of 14 different datasets related to the yeast Saccharomyces cerevisiae. In this essay, we will be discussing how to install, load and describe the Yeast datasets from the PMLB library.

The first step in using the Yeast datasets is to install the PMLB library. This can be done by running the command pip install pmlb in the command prompt or terminal. Once the library is installed, it can be imported into your Python environment by using the command import pmlb

Once the PMLB library is imported, we can load the Yeast dataset by using the command pmlb.fetch_data("yeast"). This will return a list of 14 datasets related to the yeast Saccharomyces cerevisiae. Each dataset in the list contains a different set of features and target variables.

Each dataset in the list can be described by its name, number of features, number of samples, and its target variable. For example, the first dataset in the list is the “yeast_ME2” dataset, which has 8 features, 1484 samples and the target variable is “class”, that indicates the localization site of the protein. The second dataset in the list is the “yeast_ME1” dataset, which has 8 features, 1484 samples and the target variable is “class”, that indicates the localization site of the protein.

It’s important to note that each dataset in the list has a different number of features and samples, and the target variable also varies from one dataset to another. Therefore, it’s important to carefully choose the appropriate dataset for your task and to understand the characteristics of the data.

Another important aspect when working with PMLB datasets is that these datasets are carefully curated, cleaned and preprocessed, so the user can focus on the modeling and analysis of the data rather than the data cleaning process.

In conclusion, the Yeast datasets from the PMLB library are a valuable resource for machine learning practitioners and researchers. By installing the PMLB library, loading the Yeast dataset and understanding the characteristics of each dataset, we can easily access and use a variety of datasets related to the yeast Saccharomyces cerevisiae for evaluating and comparing machine learning algorithms. These datasets are well-curated, cleaned and preprocessed, so the user can focus on the modeling and analysis of the data rather than the data cleaning process.

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: How to install, load and describe Penn Machine Learning Benchmarks – Yeast Datasets.

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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