Linnerrud Dataset
The load_linnerud function in scikit-learn provides a multi-output regression dataset containing exercise and physiological measurements from twenty middle-aged men, useful for fitness-related studies.
Samples total |
20 |
Dimensionality | 3 (for both data and target) |
Features | integer |
Targets | integer |
Linnerrud dataset Examples:
from sklearn.datasets import load_linnerud
import pandas as pd
# Load the Linnerud dataset
linnerud = load_linnerud()
# Creating DataFrames from the dataset for easier manipulation
# Features DataFrame
features_df = pd.DataFrame(data=linnerud.data, columns=linnerud.feature_names)
# Target DataFrame
targets_df = pd.DataFrame(data=linnerud.target, columns=linnerud.target_names)
# Print the first few rows of the features DataFrame
print("Features DataFrame:")
print(features_df.head())
Output:
Features DataFrame:
Chins Situps Jumps
0 5.0 162.0 60.0
1 2.0 110.0 60.0
2 12.0 101.0 101.0
3 12.0 105.0 37.0
4 13.0 155.0 58.0
What is Toy Dataset – Types, Purpose, Benefits and Application
Toy datasets are small, simple datasets commonly used in the field of machine learning for training, testing, and demonstrating algorithms. These datasets are typically clean, well-organized, and structured in a way that makes them easy to use for instructional purposes, reducing the complexities associated with real-world data processing.