from _api_doc_utils import *Logit
Binary logistic regression
1 Where it fits
Group: Regression
Logit models a binary outcome through
\[ \Pr(Y_i=1\mid X_i=x_i)=\Lambda(\alpha+x_i'\beta), \]
with optional L2 regularization controlled by alpha. predict(x) returns class labels, not probabilities.
2 Python API
Constructor: cm.Logit
Fit with integer labels using fit(x, y_int32). summary() returns intercept/coefficient estimates and Fisher-information standard errors. bootstrap() resamples observations and refits the classifier.
print(inspect.signature(cm.Logit))(alpha=1.0, max_iterations=100, gradient_tolerance=0.0001)cls = cm.Logit
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(5)
x=rng.normal(size=(220,3)); eta=-0.2+x@np.array([.7,-.4,.9]); p=1/(1+np.exp(-eta))
y=rng.binomial(1,p,size=220).astype(np.int32)
model=cm.Logit(max_iterations=200); model.fit(x,y)
print(model.summary()["coef"])
print(model.predict(x[:5]))[ 0.54200057 -0.56624631 0.82327844]
[1 0 1 0 0]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(105); x=rng.normal(size=(90,3)); p=1/(1+np.exp(-(x@np.array([.7,-.4,.9])))); y=rng.binomial(1,p,size=90).astype(np.int32)
model=cm.Logit(max_iterations=200); model.fit(x,y)
summary = model.summary()
display(HTML(html_table(["summary() key", "shape"], summary_shape_rows(summary))))| summary() key | shape |
|---|---|
intercept |
() |
coef |
(3,) |
intercept_se |
() |
coef_se |
(3,) |