Welcome to the world of DF21, a powerful and efficient implementation of the Deep Forest framework. It’s designed to outperform existing tree-based ensemble methods while ensuring ease of use and scalability for large datasets. This guide will walk you through the installation and usage of DF21, making it user-friendly even for those who are new to machine learning.

What is DF21?

DF21 stands as a promising choice among tree-based machine learning algorithms, such as Random Forest and GBDT. It features:

• Powerful: Achieves better accuracy than competitive models.
• Easy to Use: Minimal effort required for parameter tuning.
• Efficient: Fast training speed.
• Scalable: Handles large-scale data seamlessly.

How to Install DF21

Installing DF21 is a breeze with pip, the package installer for Python. Here’s how to get it up and running:

pip install deep-forest

For complete installation details, please refer to the Pip Documentation.

Quickstart Guide

Once installed, you can dive straight into classification and regression tasks. Here’s how:

Classification Example

The following analogy may help: Imagine you’re a teacher trying to evaluate the performance of several students based on their test scores (features) to see if they pass or fail (labels). DF21 functions similarly by learning from input data (test scores) to predict outcomes (pass or fail).

from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from deepforest import CascadeForestClassifier

X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
model = CascadeForestClassifier(random_state=1)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
acc = accuracy_score(y_test, y_pred) * 100
print(f'Testing Accuracy: {acc:.3f} %')

This code snippet will give you a testing accuracy of approximately 98.667%!

Regression Example

In a similar vein, think of predicting a student’s future score based on their previous results. DF21 analyzes previous performance to bridge gaps and make future predictions.

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from deepforest import CascadeForestRegressor

X, y = load_boston(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
model = CascadeForestRegressor(random_state=1)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f'Testing MSE: {mse:.3f}')

This will provide you with a Mean Squared Error of approximately 8.068.

Troubleshooting

If you encounter issues during installation or while running your code, try the following:

• Make sure you have the latest version of Python and pip installed.
• Check if any dependencies are missing; you can use the command pip install -r requirements.txt to install them.
• Ensure that you’re using the correct import statements in your script.

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Additional Resources

For comprehensive learning, refer to the following:

• Documentation
• Deep Forest: Conference | Journal
• Keynote at AISTATS 2019: Slides

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.