Библиотека собеса по Data Science | вопросы с собеседований

Напишите линейную регрессию на Python с нуля

Это один из самых простых алгоритмов. Он включает следующие шаги:
1️⃣ Инициализация параметров.
2️⃣ Вычисление предсказаний.
3️⃣ Вычисление функции потерь.
4️⃣ Обновление параметров с помощью градиентного спуска.
5️⃣ Повторение до сходимости.

import numpy as np

class LinearRegression:
    def __init__(self, learning_rate=0.01, n_iters=1000):
        self.learning_rate = learning_rate
        self.n_iters = n_iters

    def fit(self, X, y):
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features)
        self.bias = 0

        for _ in range(self.n_iters):
            model_preds = self.predict(X)
            dw = (1 / n_samples) * np.dot(X.T, (model_preds - y))
            db = (1 / n_samples) * np.sum(model_preds - y)

            self.weights -= self.learning_rate * dw
            self.bias -= self.learning_rate * db

    def predict(self, X):
        return np.dot(X, self.weights) + self.bias

#машинное_обучение
#программирование

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4.5K viewsMar 13, 2024 at 12:01

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Create: 2024-03-13
Last Update: 2025-07-02 13:07:18

import numpy as np

class LinearRegression:
    def __init__(self, learning_rate=0.01, n_iters=1000):
        self.learning_rate = learning_rate
        self.n_iters = n_iters

    def fit(self, X, y):
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features)
        self.bias = 0

        for _ in range(self.n_iters):
            model_preds = self.predict(X)
            dw = (1 / n_samples) * np.dot(X.T, (model_preds - y))
            db = (1 / n_samples) * np.sum(model_preds - y)

            self.weights -= self.learning_rate * dw
            self.bias -= self.learning_rate * db

    def predict(self, X):
        return np.dot(X, self.weights) + self.bias

#машинное_обучение
#программирование

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