tg-me.com/ds_interview_lib/611
Create:
Last Update:
Last Update:
Напишите логистическую регрессию
import numpy as np
class LogisticRegression:
def __init__(self, learning_rate=0.01, n_iters=1000):
self.learning_rate = learning_rate
self.n_iters = n_iters
self.weights = None
self.bias = None
def fit(self, X, y):
# initialize weights and bias to zeros
n_samples, n_features = X.shape
self.weights = np.zeros(n_features)
self.bias = 0
# gradient descent optimization
for i in range(self.n_iters):
# calculate predicted probabilities and cost
z = np.dot(X, self.weights) + self.bias
y_pred = self._sigmoid(z)
cost = (-1 / n_samples) * np.sum(y * np.log(y_pred) + (1 - y) * np.log(1 - y_pred))
# calculate gradients
dw = (1 / n_samples) * np.dot(X.T, (y_pred - y))
db = (1 / n_samples) * np.sum(y_pred - y)
# update weights and bias
self.weights -= self.learning_rate * dw
self.bias -= self.learning_rate * db
def predict(self, X):
# calculate predicted probabilities
z = np.dot(X, self.weights) + self.bias
y_pred = self._sigmoid(z)
# convert probabilities to binary predictions
return np.round(y_pred).astype(int)
def _sigmoid(self, z):
return 1 / (1 + np.exp(-z))
#python
#машинное_обучение
BY Библиотека собеса по Data Science | вопросы с собеседований
Warning: Undefined variable $i in /var/www/tg-me/post.php on line 283
Share with your friend now:
tg-me.com/ds_interview_lib/611