Sklearn Interactive Documentation Examples in Hugging Face Spaces
If you are a machine learning practitioner or enthusiast, you are probably familiar with Scikit-learn, a popular machine learning library in Python. Scikit-learn, also known as Sklearn, offers a wide range of machine learning algorithms and tools for data analysis and model building. In recent years, Hugging Face Spaces has become a popular platform for hosting and sharing machine learning models and datasets. Now, Hugging Face has introduced interactive documentation examples for Sklearn models in their Spaces environment, making it easier for users to explore and understand Sklearn functionalities.
Hugging Face Spaces is a collaborative and interactive platform for hosting, sharing, and exploring machine learning models and datasets. It allows users to create interactive documents, known as “Spaces” that include code, visualizations, and explanations. With the introduction of Sklearn interactive documentation examples, users can now explore and experiment with Sklearn models directly within the Hugging Face Spaces environment.
To create interactive documentation examples for Sklearn models in Hugging Face Spaces, you can use a combination of HTML and Markdown tags. Here’s an example of how you can create an interactive documentation example for a simple Sklearn model using HTML tags:
“`html
Sklearn Interactive Documentation Example
In this example, we will use a simple linear regression model from Scikit-learn to predict housing prices based on various features.
Load the Data
We will start by loading the housing dataset from Scikit-learn and exploring its features and target variables.
import numpy as np
import pandas as pd
from sklearn.datasets import load_boston
# Load the Boston housing dataset
data = load_boston()
X = pd.DataFrame(data.data, columns=data.feature_names)
y = pd.DataFrame(data.target, columns=['price'])
Build the Model
Next, we will build a simple linear regression model using Scikit-learn and fit it to the housing dataset.
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create and fit the linear regression model
model = LinearRegression()
model.fit(X_train, y_train)
Evaluate the Model
Finally, we will evaluate the performance of the model on the test set and visualize the results.
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
# Make predictions on the test set
y_pred = model.predict(X_test)
# Calculate mean squared error
mse = mean_squared_error(y_test, y_pred)
print(f"Mean squared error: {mse}")
# Visualize actual vs. predicted prices
plt.scatter(y_test, y_pred)
plt.xlabel('Actual Price')
plt.ylabel('Predicted Price')
plt.title('Actual vs. Predicted Housing Prices')
plt.show()
“`
In this example, we have used HTML tags to structure the interactive documentation example for a simple linear regression model using Sklearn. The code snippets are added using the `
` tags to maintain the formatting and syntax highlighting. The interactive nature of the example allows users to see the code, run it, and visualize the results within the Hugging Face Spaces environment.
By leveraging the powerful combination of HTML and Markdown tags, users can now create interactive documentation examples for Sklearn models in Hugging Face Spaces and share their knowledge and expertise with the machine learning community. This feature further enhances the collaborative and explorative nature of Hugging Face Spaces, making it a valuable resource for machine learning practitioners and enthusiasts alike.
