OpenBoostGAM

GPU-accelerated Generalized Additive Model - interpretable machine learning with feature-level explanations.

Why GAM?

GAMs decompose predictions into individual feature contributions:

prediction = f₁(x₁) + f₂(x₂) + ... + fₙ(xₙ) + intercept

This means you can visualize exactly how each feature affects the prediction.

Basic Usage

import openboost as ob

gam = ob.OpenBoostGAM(
    n_rounds=500,
    learning_rate=0.05,
    max_depth=3,  # Shallow trees for smoothness
)
gam.fit(X_train, y_train)
predictions = gam.predict(X_test)

# Get feature importance
importance = gam.get_feature_importance()

Visualizing Shape Functions

# Plot how each feature affects predictions
gam.plot_shape_function(
    feature_idx=0,
    feature_name="age",
)

# Plot multiple features
fig, axes = plt.subplots(2, 3, figsize=(12, 8))
for i, ax in enumerate(axes.flat):
    gam.plot_shape_function(i, ax=ax, feature_name=feature_names[i])
plt.tight_layout()

Parameters

Parameter	Type	Default	Description
n_rounds	int	1000	Number of boosting rounds
learning_rate	float	0.01	Step size
max_depth	int	3	Tree depth (keep small for smoothness)
loss	str	'mse'	Loss function

Performance vs InterpretML EBM

Samples	EBM (CPU)	OpenBoostGAM (GPU)	Speedup
10,000	3.6s	0.14s	25x
50,000	6.3s	0.16s	39x
100,000	10.5s	0.25s	43x

Example: Credit Risk

import openboost as ob
import matplotlib.pyplot as plt

# Train interpretable model
gam = ob.OpenBoostGAM(n_rounds=500, learning_rate=0.05)
gam.fit(X_train, y_train)

# Explain predictions
feature_names = ['age', 'income', 'debt_ratio', 'credit_history']

fig, axes = plt.subplots(2, 2, figsize=(12, 10))
for i, (ax, name) in enumerate(zip(axes.flat, feature_names)):
    gam.plot_shape_function(i, ax=ax, feature_name=name)
    ax.set_title(f"Effect of {name}")
plt.tight_layout()
plt.savefig("gam_explanations.png")

Best Practices

1. Use shallow trees (max_depth=3) for smoother shape functions
2. Use more rounds with lower learning rate for better fits
3. Normalize features for easier interpretation
4. Check shape functions for unexpected patterns (data issues)