वर्गीकरण (Classification): समूहों में बांटना

import numpy as np
from sklearn.linear_model import LogisticRegression

def sigmoid(z):
    return 1 / (1 + np.exp(-z))

for z in [-4, -1, 0, 1, 4]:
    print("score", z, "-> probability", round(sigmoid(z), 3))

# Made-up data: hours of practice and whether a learner passed a driving test.
hours = np.array([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]])
passed = np.array([0, 0, 0, 0, 1, 0, 1, 1, 1, 1])

model = LogisticRegression().fit(hours, passed)
for h in [2, 5, 9]:
    p = model.predict_proba([[h]])[0, 1]
    print(h, "hours: chance of passing", round(p, 2), "-> predict", model.predict([[h]])[0])

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

X, y = load_wine(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, random_state=0, stratify=y)

model = make_pipeline(StandardScaler(), LogisticRegression())
model.fit(X_train, y_train)
print("Test accuracy:", round(model.score(X_test, y_test), 3))
print("Probabilities for the first test wine:", model.predict_proba(X_test[:1]).round(3))