FTRL

Online-style follow-the-regularized-leader classifier

from _api_doc_utils import *

1 Where it fits

Group: Regression

FTRL is an online-style regularized classifier. The update combines adaptive learning rates with L1/L2 regularization, making it useful for sparse or streaming-style binary prediction problems.

The exposed class is deliberately simple: fit once on a design matrix and binary labels, then predict scores for new rows.

2 Python API

Constructor: cm.FTRL

Call FTRL(alpha, beta, l1_ratio, l2_ratio), then fit(x, y_int32). predict(x) returns scores in [0, 1]; summary() returns coefficient estimates and standard errors.

print(inspect.signature(cm.FTRL))

(alpha=0.1, beta=1.0, l1_ratio=1.0, l2_ratio=1.0)

cls = cm.FTRL
display(HTML(html_table(["Public method"], public_methods(cls))))

Public method
bootstrap(self, /, n_bootstrap, seed=None)
fit(self, /, x, y)
predict(self, /, x)
summary(self, /)

3 Minimal example

rng=np.random.default_rng(8)
x=rng.normal(size=(220,4)); p=1/(1+np.exp(-(x@np.array([.7,-.4,.2,.3])))); y=rng.binomial(1,p,size=220).astype(np.int32)
model=cm.FTRL(); model.fit(x,y)
print(model.summary()["coef"])
print(model.predict(x[:5]))

[ 0.09565982 -0.09368399  0.08623364  0.07773544]
[0.45369145 0.44605938 0.50578946 0.48307955 0.441618  ]

4 summary() contract

The table below is generated by fitting the live class in this repository and then inspecting summary(). Shapes are shown because most values are plain NumPy arrays or scalars.

rng=np.random.default_rng(108); x=rng.normal(size=(100,4)); y=rng.binomial(1,.5,size=100).astype(np.int32)
model=cm.FTRL(); model.fit(x,y)
summary = model.summary()
display(HTML(html_table(["summary() key", "shape"], summary_shape_rows(summary))))

summary() key	shape
coef	(4,)
coef_se	(4,)